1
|
Delgado-Coka L, Horowitz M, Torrente-Goncalves M, Roa-Peña L, Leiton CV, Hasan M, Babu S, Fassler D, Oentoro J, Bai JDK, Petricoin EF, Matrisian LM, Blais EM, Marchenko N, Allard FD, Jiang W, Larson B, Hendifar A, Chen C, Abousamra S, Samaras D, Kurc T, Saltz J, Escobar-Hoyos LF, Shroyer KR. Keratin 17 modulates the immune topography of pancreatic cancer. J Transl Med 2024; 22:443. [PMID: 38730319 PMCID: PMC11087249 DOI: 10.1186/s12967-024-05252-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND The immune microenvironment impacts tumor growth, invasion, metastasis, and patient survival and may provide opportunities for therapeutic intervention in pancreatic ductal adenocarcinoma (PDAC). Although never studied as a potential modulator of the immune response in most cancers, Keratin 17 (K17), a biomarker of the most aggressive (basal) molecular subtype of PDAC, is intimately involved in the histogenesis of the immune response in psoriasis, basal cell carcinoma, and cervical squamous cell carcinoma. Thus, we hypothesized that K17 expression could also impact the immune cell response in PDAC, and that uncovering this relationship could provide insight to guide the development of immunotherapeutic opportunities to extend patient survival. METHODS Multiplex immunohistochemistry (mIHC) and automated image analysis based on novel computational imaging technology were used to decipher the abundance and spatial distribution of T cells, macrophages, and tumor cells, relative to K17 expression in 235 PDACs. RESULTS K17 expression had profound effects on the exclusion of intratumoral CD8+ T cells and was also associated with decreased numbers of peritumoral CD8+ T cells, CD16+ macrophages, and CD163+ macrophages (p < 0.0001). The differences in the intratumor and peritumoral CD8+ T cell abundance were not impacted by neoadjuvant therapy, tumor stage, grade, lymph node status, histologic subtype, nor KRAS, p53, SMAD4, or CDKN2A mutations. CONCLUSIONS Thus, K17 expression correlates with major differences in the immune microenvironment that are independent of any tested clinicopathologic or tumor intrinsic variables, suggesting that targeting K17-mediated immune effects on the immune system could restore the innate immunologic response to PDAC and might provide novel opportunities to restore immunotherapeutic approaches for this most deadly form of cancer.
Collapse
Affiliation(s)
- Lyanne Delgado-Coka
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
- Program of Public Health and Department of Preventative Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Michael Horowitz
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Mariana Torrente-Goncalves
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Lucia Roa-Peña
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Pathology, School of Medicine, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Cindy V Leiton
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Mahmudul Hasan
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Sruthi Babu
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Danielle Fassler
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Jaymie Oentoro
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Ji-Dong K Bai
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Emanuel F Petricoin
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Fairfax, VA, USA
- Perthera, McLean, VA, USA
| | - Lynn M Matrisian
- Scientific and Medical Affairs, Pancreatic Cancer Action Network, Manhattan Beach, CA, USA
| | | | - Natalia Marchenko
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Felicia D Allard
- Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Wei Jiang
- Department of Pathology and Genomic Medicine, Sidney Kimmel Cancer Center, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Brent Larson
- Departments of Pathology and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrew Hendifar
- Departments of Pathology and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Chao Chen
- Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Shahira Abousamra
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Joel Saltz
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA.
- Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA.
| | - Luisa F Escobar-Hoyos
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA.
- Department of Therapeutic Radiology, Yale University, New Haven, CT, USA.
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
- Division of Oncology, Department of Medicine, Yale University, New Haven, CT, USA.
| | - Kenneth R Shroyer
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794, USA.
| |
Collapse
|
2
|
Delgado-Coka LA, Horowitz M, Torrente-Goncalves M, Roa-Peña L, Leiton CV, Hasan M, Babu S, Fassler D, Oentoro J, Karen Bai JD, Petricoin EF, Matrisian LM, Blais EM, Marchenko N, Allard FD, Jiang W, Larson B, Hendifar A, Chen C, Abousamra S, Samaras D, Kurc T, Saltz J, Escobar-Hoyos LF, Shroyer K. Keratin 17 modulates the immune topography of pancreatic cancer. Res Sq 2024:rs.3.rs-3886691. [PMID: 38464123 PMCID: PMC10925455 DOI: 10.21203/rs.3.rs-3886691/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Background The immune microenvironment impacts tumor growth, invasion, metastasis, and patient survival and may provide opportunities for therapeutic intervention in pancreatic ductal adenocarcinoma (PDAC). Although never studied as a potential modulator of the immune response in most cancers, Keratin 17 (K17), a biomarker of the most aggressive (basal) molecular subtype of PDAC, is intimately involved in the histogenesis of the immune response in psoriasis, basal cell carcinoma, and cervical squamous cell carcinoma. Thus, we hypothesized that K17 expression could also impact the immune cell response in PDAC, and that uncovering this relationship could provide insight to guide the development of immunotherapeutic opportunities to extend patient survival. Methods Multiplex immunohistochemistry (mIHC) and automated image analysis based on novel computational imaging technology were used to decipher the abundance and spatial distribution of T cells, macrophages, and tumor cells, relative to K17 expression in 235 PDACs. Results K17 expression had profound effects on the exclusion of intratumoral CD8 + T cells and was also associated with decreased numbers of peritumoral CD8 + T cells, CD16 + macrophages, and CD163 + macrophages (p < 0.0001). The differences in the intratumor and peritumoral CD8 + T cell abundance were not impacted by neoadjuvant therapy, tumor stage, grade, lymph node status, histologic subtype, nor KRAS, p53, SMAD4, or CDKN2A mutations. Conclusions Thus, K17 expression correlates with major differences in the immune microenvironment that are independent of any tested clinicopathologic or tumor intrinsic variables, suggesting that targeting K17-mediated immune effects on the immune system could restore the innate immunologic response to PDAC and might provide novel opportunities to restore immunotherapeutic approaches for this most deadly form of cancer.
Collapse
|
3
|
Foran DJ, Chen W, Kurc T, Gupta R, Kaczmarzyk JR, Torre-Healy LA, Bremer E, Ajjarapu S, Do N, Harris G, Stroup A, Durbin E, Saltz JH. An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing. Cancer Inform 2024; 23:11769351231223806. [PMID: 38322427 PMCID: PMC10840403 DOI: 10.1177/11769351231223806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/14/2023] [Indexed: 02/08/2024] Open
Abstract
Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.
Collapse
Affiliation(s)
- David J Foran
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Wenjin Chen
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, The State University of New York, Stony Brook, NY, USA
| | - Rajarshi Gupta
- Department of Biomedical Informatics, Stony Brook University, The State University of New York, Stony Brook, NY, USA
| | | | | | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook University, The State University of New York, Stony Brook, NY, USA
| | | | - Nhan Do
- VA Healthcare System Jamaica Plain Campus, Boston, MA, USA
| | - Gerald Harris
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Antoinette Stroup
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Eric Durbin
- Kentucky Cancer Registry, Markey Cancer Center, Lexington, KY, USA
| | - Joel H Saltz
- Department of Biomedical Informatics, Stony Brook University, The State University of New York, Stony Brook, NY, USA
| |
Collapse
|
4
|
Reinke A, Tizabi MD, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Kavur AE, Rädsch T, Sudre CH, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Buettner F, Cardoso MJ, Cheplygina V, Chen J, Christodoulou E, Cimini BA, Farahani K, Ferrer L, Galdran A, van Ginneken B, Glocker B, Godau P, Hashimoto DA, Hoffman MM, Huisman M, Isensee F, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Kleesiek J, Kofler F, Kooi T, Kopp-Schneider A, Kozubek M, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rafelski SM, Rajpoot N, Reyes M, Riegler MA, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Yaniv ZR, Jäger PF, Maier-Hein L. Understanding metric-related pitfalls in image analysis validation. Nat Methods 2024; 21:182-194. [PMID: 38347140 DOI: 10.1038/s41592-023-02150-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/12/2023] [Indexed: 02/15/2024]
Abstract
Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.
Collapse
Affiliation(s)
- Annika Reinke
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Minu D Tizabi
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
| | - Michael Baumgartner
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Matthias Eisenmann
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Doreen Heckmann-Nötzel
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - A Emre Kavur
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Tim Rädsch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Laura Acion
- Instituto de Cálculo, CONICET - Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Tal Arbel
- Centre for Intelligent Machines and MILA (Quebec Artificial Intelligence Institute), McGill University, Montréal, Quebec, Canada
| | - Spyridon Bakas
- Division of Computational Pathology, Dept of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Arriel Benis
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel
- European Federation for Medical Informatics, Le Mont-sur-Lausanne, Switzerland
| | - Florian Buettner
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Frankfurt am Main, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Goethe University Frankfurt, Department of Medicine, Frankfurt am Main, Germany
- Goethe University Frankfurt, Department of Informatics, Frankfurt am Main, Germany
- Frankfurt Cancer Insititute, Frankfurt am Main, Germany
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Veronika Cheplygina
- Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | - Jianxu Chen
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
| | - Evangelia Christodoulou
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
| | - Luciana Ferrer
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Adrian Galdran
- Universitat Pompeu Fabra, Barcelona, Spain
- University of Adelaide, Adelaide, South Australia, Australia
| | - Bram van Ginneken
- Fraunhofer MEVIS, Bremen, Germany
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ben Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Patrick Godau
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Daniel A Hashimoto
- Department of Surgery, Perelman School of Medicine, Philadelphia, PA, USA
- General Robotics Automation Sensing and Perception Laboratory, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Pierre Jannin
- Laboratoire Traitement du Signal et de l'Image - UMR_S 1099, Université de Rennes 1, Rennes, France
- INSERM, Paris, France
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dagmar Kainmueller
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Biomedical Image Analysis and HI Helmholtz Imaging, Berlin, Germany
- University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Bernhard Kainz
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
- Department AIBE, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany
| | | | - Jens Kleesiek
- Translational Image-guided Oncology (TIO), Institute for AI in Medicine (IKIM), University Medicine Essen, Essen, Germany
| | | | | | - Annette Kopp-Schneider
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis and Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Health Science Center, Stony Brook, NY, USA
| | | | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Amin Madani
- Department of Surgery, University Health Network, Philadelphia, PA, USA
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne L Martel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, UNSW Sydney, Kensington, New South Wales, Australia
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
- Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Brennan Nichyporuk
- MILA (Quebec Artificial Intelligence Institute), Montréal, Quebec, Canada
| | - Felix Nickel
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Petersen
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | | | - Nasir Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, UK
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- UiT The Arctic University of Norway, Tromsø, Norway
| | | | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Clara I Sánchez
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Abdel A Taha
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
| | - Aleksei Tiulpin
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Neurocenter Oulu, Oulu University Hospital, Oulu, Finland
| | | | - Ben Van Calster
- Department of Development and Regeneration and EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Gaël Varoquaux
- Parietal project team, INRIA Saclay-Île de France, Palaiseau, France
| | - Ziv R Yaniv
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - Paul F Jäger
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group, Heidelberg, Germany.
| | - Lena Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany.
| |
Collapse
|
5
|
Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, Glocker B, Isensee F, Kleesiek J, Kozubek M, Reyes M, Riegler MA, Wiesenfarth M, Kavur AE, Sudre CH, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Rädsch T, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Blaschko MB, Cardoso MJ, Cheplygina V, Cimini BA, Collins GS, Farahani K, Ferrer L, Galdran A, van Ginneken B, Haase R, Hashimoto DA, Hoffman MM, Huisman M, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Karthikesalingam A, Kofler F, Kopp-Schneider A, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Mattson P, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rajpoot N, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, van Smeden M, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Jäger PF. Metrics reloaded: recommendations for image analysis validation. Nat Methods 2024; 21:195-212. [PMID: 38347141 DOI: 10.1038/s41592-023-02151-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/12/2023] [Indexed: 02/15/2024]
Abstract
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.
Collapse
Affiliation(s)
- Lena Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
- Medical Faculty, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
| | - Annika Reinke
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Patrick Godau
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Minu D Tizabi
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Florian Buettner
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Frankfurt am Main, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Department of Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany
- Department of Informatics, Goethe University Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Insititute, Frankfurt am Main, Germany
| | - Evangelia Christodoulou
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Ben Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine, University Medicine Essen, Essen, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis and Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
| | - Manuel Wiesenfarth
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - A Emre Kavur
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Michael Baumgartner
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Matthias Eisenmann
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Doreen Heckmann-Nötzel
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Tim Rädsch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany
| | - Laura Acion
- Instituto de Cálculo, CONICET - Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Tal Arbel
- Centre for Intelligent Machines and MILA (Québec Artificial Intelligence Institute), McGill University, Montréal, Quebec, Canada
| | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, IU Health Information and Translational Sciences Building, Indianapolis, IN, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Arriel Benis
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel
- European Federation for Medical Informatics, Le Mont-sur-Lausanne, Switzerland
| | - Matthew B Blaschko
- Center for Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Veronika Cheplygina
- Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gary S Collins
- Centre for Statistics in Medicine, University of Oxford, Nuffield Orthopaedic Centre, Oxford, UK
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
| | - Luciana Ferrer
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Adrian Galdran
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain
- Australian Institute for Machine Learning AIML, University of Adelaide, Adelaide, South Australia, Australia
| | - Bram van Ginneken
- Fraunhofer MEVIS, Bremen, Germany
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Robert Haase
- Technische Universität (TU) Dresden, DFG Cluster of Excellence 'Physics of Life', Dresden, Germany
- Center for Systems Biology, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig University, Leipzig, Germany
| | - Daniel A Hashimoto
- Department of Surgery, Perelman School of Medicine, Philadelphia, PA, USA
- General Robotics Automation Sensing and Perception Laboratory, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Pierre Jannin
- Laboratoire Traitement du Signal et de l'Image - UMR_S 1099, Université de Rennes 1, Rennes, France
- INSERM, Paris, France
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dagmar Kainmueller
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Biomedical Image Analysis and HI Helmholtz Imaging, Berlin, Germany
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Bernhard Kainz
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
- Department AIBE, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany
| | | | | | | | - Annette Kopp-Schneider
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Health Science Center, Stony Brook, NY, USA
| | | | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Amin Madani
- Department of Surgery, University Health Network, Philadelphia, PA, USA
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne L Martel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Peter Mattson
- Google, 1600 Amphitheatre Pkwy, Mountain View, CA, USA
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, UNSW Sydney, Kensington, New South Wales, Australia
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
- Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Brennan Nichyporuk
- MILA (Québec Artificial Intelligence Institute), Montréal, Quebec, Canada
| | - Felix Nickel
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Petersen
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Nasir Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, UK
| | | | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Clara I Sánchez
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Abdel A Taha
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
| | - Aleksei Tiulpin
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Neurocenter Oulu, Oulu University Hospital, Oulu, Finland
| | | | - Ben Van Calster
- Department of Development and Regeneration and EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Gaël Varoquaux
- Parietal project team, INRIA Saclay-Île de France, Palaiseau, France
| | - Paul F Jäger
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group, Heidelberg, Germany.
| |
Collapse
|
6
|
Kaczmarzyk JR, O'Callaghan A, Inglis F, Gat S, Kurc T, Gupta R, Bremer E, Bankhead P, Saltz JH. Open and reusable deep learning for pathology with WSInfer and QuPath. NPJ Precis Oncol 2024; 8:9. [PMID: 38200147 PMCID: PMC10781748 DOI: 10.1038/s41698-024-00499-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
Digital pathology has seen a proliferation of deep learning models in recent years, but many models are not readily reusable. To address this challenge, we developed WSInfer: an open-source software ecosystem designed to streamline the sharing and reuse of deep learning models for digital pathology. The increased access to trained models can augment research on the diagnostic, prognostic, and predictive capabilities of digital pathology.
Collapse
Affiliation(s)
- Jakub R Kaczmarzyk
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
| | - Alan O'Callaghan
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Fiona Inglis
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Swarad Gat
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Peter Bankhead
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Edinburgh Pathology and CRUK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Joel H Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
7
|
Abousamra S, Gupta R, Kurc T, Samaras D, Saltz J, Chen C. Topology-Guided Multi-Class Cell Context Generation for Digital Pathology. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2023; 2023:3323-3333. [PMID: 38741683 PMCID: PMC11090253 DOI: 10.1109/cvpr52729.2023.00324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.
Collapse
Affiliation(s)
| | - Rajarsi Gupta
- Stony Brook University, Department of Biomedical Informatics, USA
| | - Tahsin Kurc
- Stony Brook University, Department of Biomedical Informatics, USA
| | | | - Joel Saltz
- Stony Brook University, Department of Biomedical Informatics, USA
| | - Chao Chen
- Stony Brook University, Department of Biomedical Informatics, USA
| |
Collapse
|
8
|
Meirelles ALS, Kurc T, Kong J, Ferreira R, Saltz J, Teodoro G. Effective and Efficient Active Learning for Deep Learning Based Tissue Image Analysis. Bioinformatics 2023; 39:7082521. [PMID: 36943380 PMCID: PMC10079352 DOI: 10.1093/bioinformatics/btad138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 02/28/2023] [Accepted: 03/17/2023] [Indexed: 03/23/2023] Open
Abstract
MOTIVATION Deep learning attained excellent results in Digital Pathology recently. A challenge with its use is that high quality, representative training data sets are required to build robust models. Data annotation in the domain is labor intensive and demands substantial time commitment from expert pathologists. Active Learning (AL) is a strategy to minimize annotation. The goal is to select samples from the pool of unlabeled data for annotation that improves model accuracy. However, AL is a very compute demanding approach. The benefits for model learning may vary according to the strategy used, and it may be hard for a domain specialist to fine tune the solution without an integrated interface. RESULTS We developed a framework that includes a friendly user interface along with run-time optimizations to reduce annotation and execution time in AL in digital pathology. Our solution implements several AL strategies along with our Diversity-Aware Data Acquisition (DADA) acquisition function, which enforces data diversity to improve the prediction performance of a model. In this work, we employed a model simplification strategy (Network Auto-Reduction (NAR)) that significantly improves AL execution time when coupled with DADA. NAR produces less compute demanding models, which replace the target models during the AL process to reduce processing demands. An evaluation with a Tumor-Infiltrating Lymphocytes (TILs) classification application shows that: (i) DADA attains superior performance compared to state-of-the-art AL strategies for different Convolutional Neural Networks (CNNs), (ii) NAR improves the AL execution time by up to 4.3 ×, and (iii) target models trained with patches/data selected by the NAR reduced versions achieve similar or superior classification quality to using target CNNs for data selection. AVAILABILITY Source code: https://github.com/alsmeirelles/DADA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- André L S Meirelles
- Department of Computer Science, University of Brasília, Brasília, 70910-900, Brazil
| | - Tahsin Kurc
- Biomedical Informatics Department, Stony Brook University, Stony Brook, 11794-8322, USA
| | - Jun Kong
- Department of Mathematics and Statistics and Computer Science, Georgia State University, Atlanta, GA, USA
| | - Renato Ferreira
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Joel Saltz
- Biomedical Informatics Department, Stony Brook University, Stony Brook, 11794-8322, USA
| | - George Teodoro
- Department of Computer Science, University of Brasília, Brasília, 70910-900, Brazil
- Biomedical Informatics Department, Stony Brook University, Stony Brook, 11794-8322, USA
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| |
Collapse
|
9
|
Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nat Commun 2023; 14:436. [PMID: 36702828 PMCID: PMC9879935 DOI: 10.1038/s41467-023-36188-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
10
|
Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
Collapse
Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
11
|
Liu H, Kurc T. Deep learning for survival analysis in breast cancer with whole slide image data. Bioinformatics 2022; 38:3629-3637. [PMID: 35674341 PMCID: PMC9272797 DOI: 10.1093/bioinformatics/btac381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/22/2022] [Accepted: 06/04/2022] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Whole slide tissue images contain detailed data on the sub-cellular structure of cancer. Quantitative analyses of this data can lead to novel biomarkers for better cancer diagnosis and prognosis and can improve our understanding of cancer mechanisms. Such analyses are challenging to execute because of the sizes and complexity of whole slide image data and relatively limited volume of training data for machine learning methods. RESULTS We propose and experimentally evaluate a multi-resolution deep learning method for breast cancer survival analysis. The proposed method integrates image data at multiple resolutions and tumor, lymphocyte and nuclear segmentation results from deep learning models. Our results show that this approach can significantly improve the deep learning model performance compared to using only the original image data. The proposed approach achieves a c-index value of 0.706 compared to a c-index value of 0.551 from an approach that uses only color image data at the highest image resolution. Furthermore, when clinical features (sex, age and cancer stage) are combined with image data, the proposed approach achieves a c-index of 0.773. AVAILABILITY AND IMPLEMENTATION https://github.com/SBU-BMI/deep_survival_analysis.
Collapse
Affiliation(s)
- Huidong Liu
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| |
Collapse
|
12
|
Meirelles AL, Kurc T, Saltz J, Teodoro G. Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes. Comput Methods Programs Biomed 2022; 220:106828. [PMID: 35500506 DOI: 10.1016/j.cmpb.2022.106828] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 04/09/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Deep learning methods have demonstrated remarkable performance in pathology image analysis, but they require a large amount of annotated training data from expert pathologists. The aim of this study is to minimize the data annotation need in these analyses. METHODS Active learning (AL) is an iterative approach to training deep learning models. It was used in our context with a Tumor Infiltrating Lymphocytes (TIL) classification task to minimize annotation. State-of-the-art AL methods were evaluated with the TIL application and we have proposed and evaluated a more efficient and effective AL acquisition method. The proposed method uses data grouping based on imaging features and model prediction uncertainty to select meaningful training samples (image patches). RESULTS An experimental evaluation with a collection of cancer tissue images shows that: (i) Our approach reduces the number of patches required to attain a given AUC as compared to other approaches, and (ii) our optimization (subpooling) leads to AL execution time improvement of about 2.12×. CONCLUSIONS This strategy enabled TIL based deep learning analyses using smaller annotation demand. We expect this approach may be used to build other analyses in digital pathology with fewer training samples.
Collapse
Affiliation(s)
- André Ls Meirelles
- Department of Computer Science, University of Brasília, Brasília, 70910-900, Brazil
| | - Tahsin Kurc
- Biomedical Informatics Department, Stony Brook University, Stony Brook, 11794-8322, USA
| | - Joel Saltz
- Biomedical Informatics Department, Stony Brook University, Stony Brook, 11794-8322, USA
| | - George Teodoro
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil.
| |
Collapse
|
13
|
Meirelles ALS, Kurc T, Kong J, Ferreira R, Saltz JH, Teodoro G. Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification. Front Med (Lausanne) 2022; 9:894430. [PMID: 35712087 PMCID: PMC9197439 DOI: 10.3389/fmed.2022.894430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background Deep learning methods have demonstrated remarkable performance in pathology image analysis, but they are computationally very demanding. The aim of our study is to reduce their computational cost to enable their use with large tissue image datasets. Methods We propose a method called Network Auto-Reduction (NAR) that simplifies a Convolutional Neural Network (CNN) by reducing the network to minimize the computational cost of doing a prediction. NAR performs a compound scaling in which the width, depth, and resolution dimensions of the network are reduced together to maintain a balance among them in the resulting simplified network. We compare our method with a state-of-the-art solution called ResRep. The evaluation is carried out with popular CNN architectures and a real-world application that identifies distributions of tumor-infiltrating lymphocytes in tissue images. Results The experimental results show that both ResRep and NAR are able to generate simplified, more efficient versions of ResNet50 V2. The simplified versions by ResRep and NAR require 1.32× and 3.26× fewer floating-point operations (FLOPs), respectively, than the original network without a loss in classification power as measured by the Area under the Curve (AUC) metric. When applied to a deeper and more computationally expensive network, Inception V4, NAR is able to generate a version that requires 4× lower than the original version with the same AUC performance. Conclusions NAR is able to achieve substantial reductions in the execution cost of two popular CNN architectures, while resulting in small or no loss in model accuracy. Such cost savings can significantly improve the use of deep learning methods in digital pathology. They can enable studies with larger tissue image datasets and facilitate the use of less expensive and more accessible graphics processing units (GPUs), thus reducing the computing costs of a study.
Collapse
Affiliation(s)
| | - Tahsin Kurc
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY, United States
| | - Jun Kong
- Department of Mathematics and Statistics and Computer Science, Georgia State University, Atlanta, GA, United States
| | - Renato Ferreira
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Joel H. Saltz
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY, United States
| | - George Teodoro
- Department of Computer Science, Universidade de Brasília, Brasília, Brazil
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| |
Collapse
|
14
|
Fassler DJ, Torre-Healy LA, Gupta R, Hamilton AM, Kobayashi S, Van Alsten SC, Zhang Y, Kurc T, Moffitt RA, Troester MA, Hoadley KA, Saltz J. Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression. Cancers (Basel) 2022; 14:2148. [PMID: 35565277 PMCID: PMC9105398 DOI: 10.3390/cancers14092148] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/09/2022] [Accepted: 04/15/2022] [Indexed: 12/15/2022] Open
Abstract
Tumor-infiltrating lymphocytes (TILs) have been established as a robust prognostic biomarker in breast cancer, with emerging utility in predicting treatment response in the adjuvant and neoadjuvant settings. In this study, the role of TILs in predicting overall survival and progression-free interval was evaluated in two independent cohorts of breast cancer from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). We utilized machine learning and computer vision algorithms to characterize TIL infiltrates in digital whole-slide images (WSIs) of breast cancer stained with hematoxylin and eosin (H&E). Multiple parameters were used to characterize the global abundance and spatial features of TIL infiltrates. Univariate and multivariate analyses show that large aggregates of peritumoral and intratumoral TILs (forests) were associated with longer survival, whereas the absence of intratumoral TILs (deserts) is associated with increased risk of recurrence. Patients with two or more high-risk spatial features were associated with significantly shorter progression-free interval (PFI). This study demonstrates the practical utility of Pathomics in evaluating the clinical significance of the abundance and spatial patterns of distribution of TIL infiltrates as important biomarkers in breast cancer.
Collapse
Affiliation(s)
- Danielle J. Fassler
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Luke A. Torre-Healy
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Alina M. Hamilton
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (A.M.H.); (S.C.V.A.); (M.A.T.)
| | - Soma Kobayashi
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Sarah C. Van Alsten
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (A.M.H.); (S.C.V.A.); (M.A.T.)
| | - Yuwei Zhang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Richard A. Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Melissa A. Troester
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (A.M.H.); (S.C.V.A.); (M.A.T.)
| | - Katherine A. Hoadley
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| |
Collapse
|
15
|
Abousamra S, Gupta R, Hou L, Batiste R, Zhao T, Shankar A, Rao A, Chen C, Samaras D, Kurc T, Saltz J. Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer. Front Oncol 2022; 11:806603. [PMID: 35251953 PMCID: PMC8889499 DOI: 10.3389/fonc.2021.806603] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/31/2021] [Indexed: 12/12/2022] Open
Abstract
The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease progression and clinical outcomes has generated tremendous interest in translational cancer research. We present an updated and enhanced deep learning workflow to classify 50x50 um tiled image patches (100x100 pixels at 20x magnification) as TIL positive or negative based on the presence of 2 or more TILs in gigapixel whole slide images (WSIs) from the Cancer Genome Atlas (TCGA). This workflow generates TIL maps to study the abundance and spatial distribution of TILs in 23 different types of cancer. We trained three state-of-the-art, popular convolutional neural network (CNN) architectures (namely VGG16, Inception-V4, and ResNet-34) with a large volume of training data, which combined manual annotations from pathologists (strong annotations) and computer-generated labels from our previously reported first-generation TIL model for 13 cancer types (model-generated annotations). Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. We report these new TIL models and a curated dataset of TIL maps, referred to as TIL-Maps-23, for 7983 WSIs spanning 23 types of cancer with complex and diverse visual appearances, which will be publicly available along with the code to evaluate performance. Code Available at:https://github.com/ShahiraAbousamra/til_classification.
Collapse
Affiliation(s)
- Shahira Abousamra
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Le Hou
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Rebecca Batiste
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Tianhao Zhao
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Anand Shankar
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Arvind Rao
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Chao Chen
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| |
Collapse
|
16
|
Foran DJ, Durbin EB, Chen W, Sadimin E, Sharma A, Banerjee I, Kurc T, Li N, Stroup AM, Harris G, Gu A, Schymura M, Gupta R, Bremer E, Balsamo J, DiPrima T, Wang F, Abousamra S, Samaras D, Hands I, Ward K, Saltz JH. An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities. J Pathol Inform 2022; 13:5. [PMID: 35136672 PMCID: PMC8794027 DOI: 10.4103/jpi.jpi_31_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 04/30/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). MATERIALS AND METHODS As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. RESULTS Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. CONCLUSION To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.
Collapse
Affiliation(s)
- David J. Foran
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Eric B. Durbin
- Kentucky Cancer Registry, Markey Cancer Center, University of Kentucky, Lexington, KY, USA
- Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
| | - Wenjin Chen
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Evita Sadimin
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Imon Banerjee
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Nan Li
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Antoinette M. Stroup
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Gerald Harris
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Annie Gu
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Maria Schymura
- New York State Cancer Registry, New York State Department of Health, Albany, NY, USA
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Joseph Balsamo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Tammy DiPrima
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Feiqiao Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Shahira Abousamra
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Isaac Hands
- Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
| | - Kevin Ward
- Georgia State Cancer Registry, Georgia Department of Public Health, Atlanta, GA, USA
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
17
|
Agraz JL, Grenko CM, Chen AA, Viaene AN, Nasrallah MD, Pati S, Kurc T, Saltz J, Feldman MD, Akbari H, Sharma P, Shinohara RT, Bakas S. Robust Image Population Based Stain Color Normalization: How Many Reference Slides Are Enough? IEEE Open J Eng Med Biol 2022; 3:218-226. [PMID: 36860498 PMCID: PMC9970045 DOI: 10.1109/ojemb.2023.3234443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 11/08/2022] [Accepted: 01/01/2023] [Indexed: 01/06/2023] Open
Abstract
Histopathologic evaluation of Hematoxylin & Eosin (H&E) stained slides is essential for disease diagnosis, revealing tissue morphology, structure, and cellular composition. Variations in staining protocols and equipment result in images with color nonconformity. Although pathologists compensate for color variations, these disparities introduce inaccuracies in computational whole slide image (WSI) analysis, accentuating data domain shift and degrading generalization. Current state-of-the-art normalization methods employ a single WSI as reference, but selecting a single WSI representative of a complete WSI-cohort is infeasible, inadvertently introducing normalization bias. We seek the optimal number of slides to construct a more representative reference based on composite/aggregate of multiple H&E density histograms and stain-vectors, obtained from a randomly selected WSI population (WSI-Cohort-Subset). We utilized 1,864 IvyGAP WSIs as a WSI-cohort, and built 200 WSI-Cohort-Subsets varying in size (from 1 to 200 WSI-pairs) using randomly selected WSIs. The WSI-pairs' mean Wasserstein Distances and WSI-Cohort-Subsets' standard deviations were calculated. The Pareto Principle defined the optimal WSI-Cohort-Subset size. The WSI-cohort underwent structure-preserving color normalization using the optimal WSI-Cohort-Subset histogram and stain-vector aggregates. Numerous normalization permutations support WSI-Cohort-Subset aggregates as representative of a WSI-cohort through WSI-cohort CIELAB color space swift convergence, as a result of the law of large numbers and shown as a power law distribution. We show normalization at the optimal (Pareto Principle) WSI-Cohort-Subset size and corresponding CIELAB convergence: a) Quantitatively, using 500 WSI-cohorts; b) Quantitatively, using 8,100 WSI-regions; c) Qualitatively, using 30 cellular tumor normalization permutations. Aggregate-based stain normalization may contribute in increasing computational pathology robustness, reproducibility, and integrity.
Collapse
Affiliation(s)
- Jose L Agraz
- Center for Biomedical Image Computing and Analytics (CBICA) Philaldelphia PA 19139 USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine Philaldelphia PA 19139 USA.,Department of Radiology at Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - Caleb M Grenko
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania and the Center for Interdisciplinary Studies Davidson College NC 28035 USA
| | - Andrew A Chen
- Penn Statistical Imaging and Visualization Endeavor (PennSIVE)University of Pennsylvania Philaldelphia PA 19139 USA
| | - Angela N Viaene
- Department of Pathology and Laboratory Medicine, Children's Hospital of PhiladelphiaUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - MacLean D Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - Sarthak Pati
- CBICA and Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA.,Department of Radiology at Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - Tahsin Kurc
- Department of Biomedical InformaticsStony Brook University Stony Brook NY 11794-0751 USA
| | - Joel Saltz
- Department of Biomedical InformaticsStony Brook University Stony Brook NY 11794-0751 USA
| | - Michael D Feldman
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - Hamed Akbari
- CBICA and the Department of Radiology, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | | | - Russell T Shinohara
- CBICA and the Penn Statistical Imaging and Visualization Endeavor (PennSIVE)University of Pennsylvania Philaldelphia PA 19139 USA
| | - Spyridon Bakas
- CBICA, and the Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA.,Department of Radiology, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| |
Collapse
|
18
|
Gomes J, Kong J, Kurc T, Melo ACMA, Ferreira R, Saltz JH, Teodoro G. Building robust pathology image analyses with uncertainty quantification. Comput Methods Programs Biomed 2021; 208:106291. [PMID: 34333205 DOI: 10.1016/j.cmpb.2021.106291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Computerized pathology image analysis is an important tool in research and clinical settings, which enables quantitative tissue characterization and can assist a pathologist's evaluation. The aim of our study is to systematically quantify and minimize uncertainty in output of computer based pathology image analysis. METHODS Uncertainty quantification (UQ) and sensitivity analysis (SA) methods, such as Variance-Based Decomposition (VBD) and Morris One-At-a-Time (MOAT), are employed to track and quantify uncertainty in a real-world application with large Whole Slide Imaging datasets - 943 Breast Invasive Carcinoma (BRCA) and 381 Lung Squamous Cell Carcinoma (LUSC) patients. Because these studies are compute intensive, high-performance computing systems and efficient UQ/SA methods were combined to provide efficient execution. UQ/SA has been able to highlight parameters of the application that impact the results, as well as nuclear features that carry most of the uncertainty. Using this information, we built a method for selecting stable features that minimize application output uncertainty. RESULTS The results show that input parameter variations significantly impact all stages (segmentation, feature computation, and survival analysis) of the use case application. We then identified and classified features according to their robustness to parameter variation, and using the proposed features selection strategy, for instance, patient grouping stability in survival analysis has been improved from in 17% and 34% for BRCA and LUSC, respectively. CONCLUSIONS This strategy created more robust analyses, demonstrating that SA and UQ are important methods that may increase confidence digital pathology.
Collapse
Affiliation(s)
- Jeremias Gomes
- Department of Computer Science, University of Brasília, Brasília, Brazil
| | - Jun Kong
- Biomedical Informatics Department, Emory University, Atlanta, USA; Department of Biomedical Engineering, Emory-Georgia Institute of Technology, Atlanta, USA; Department of Mathematics and Statistics, Georgia State University, Atlanta, USA
| | - Tahsin Kurc
- Biomedical Informatics Department, Stony Brook University, Stony Brook, USA; Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, USA
| | - Alba C M A Melo
- Department of Computer Science, University of Brasília, Brasília, Brazil
| | - Renato Ferreira
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Joel H Saltz
- Biomedical Informatics Department, Stony Brook University, Stony Brook, USA
| | - George Teodoro
- Department of Computer Science, University of Brasília, Brasília, Brazil; Biomedical Informatics Department, Stony Brook University, Stony Brook, USA; Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
| |
Collapse
|
19
|
Sharma A, Tarbox L, Kurc T, Bona J, Smith K, Kathiravelu P, Bremer E, Saltz JH, Prior F. PRISM: A Platform for Imaging in Precision Medicine. JCO Clin Cancer Inform 2021; 4:491-499. [PMID: 32479186 PMCID: PMC7328100 DOI: 10.1200/cci.20.00001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community. METHODS A modular technology platform is presented that would allow existing data resources, such as TCIA, to evolve into a comprehensive data resource that meets the needs of users engaged in translational research for imaging-based precision medicine. This Platform for Imaging in Precision Medicine (PRISM) helps streamline the deployment and improve TCIA's efficiency and sustainability. More importantly, its inherent modular architecture facilitates a piecemeal adoption by other data repositories. RESULTS PRISM includes services for managing radiology and pathology images and features and associated clinical data. A semantic layer is being built to help users explore diverse collections and pool data sets to create specialized cohorts. PRISM includes tools for image curation and de-identification. It includes image visualization and feature exploration tools. The entire platform is distributed as a series of containerized microservices with representational state transfer interfaces. CONCLUSION PRISM is helping modernize, scale, and sustain the technology stack that powers TCIA. Repositories can take advantage of individual PRISM services such as de-identification and quality control. PRISM is helping scale image informatics for cancer research at a time when the size, complexity, and demands to integrate image data with other precision medicine data-intensive commons are mounting.
Collapse
Affiliation(s)
| | - Lawrence Tarbox
- University of Arkansas for Medical Sciences, Little Rock, AR
| | | | - Jonathan Bona
- University of Arkansas for Medical Sciences, Little Rock, AR
| | - Kirk Smith
- University of Arkansas for Medical Sciences, Little Rock, AR
| | | | | | | | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR
| |
Collapse
|
20
|
Mallipattu SK, Jawa R, Moffitt R, Hajagos J, Fries B, Nachman S, Gan TJ, Saltz M, Saltz J, Kaushansky K, Skopicki H, Abell-Hart K, Chaudhri I, Deng J, Garcia V, Gayen S, Kurc T, Bolotova O, Yoo J, Dhaliwal S, Nataraj N, Sun S, Tsai C, Wang Y, Abbasi S, Abdullah R, Ahmad S, Bai K, Bennett-Guerrero E, Chua A, Gomes C, Griffel M, Kalogeropoulos A, Kiamanesh D, Kim N, Koraishy F, Lingham V, Mansour M, Marcos L, Miller J, Poovathor S, Rubano J, Rutigliano D, Sands M, Santora C, Schwartz J, Shroyer K, Spitzer S, Stopeck A, Talamini M, Tharakan M, Vosswinkel J, Wertheim W, Mallipattu SK, Jawa R, Moffitt R, Hajagos J, Fries B, Nachman S, Gan TJ, Saltz M, Saltz J, Kaushansky K, Skopicki H, Abell-Hart K, Chaudhri I, Deng J, Garcia V, Gayen S, Kurc T, Bolotova O, Yoo J, Dhaliwal S, Nataraj N, Sun S, Tsai C, Wang Y, Abbasi S, Abdullah R, Ahmad S, Bai K, Bennett-Guerrero E, Chua A, Gomes C, Griffel M, Kalogeropoulos A, Kiamanesh D, Kim N, Koraishy F, Lingham V, Mansour M, Marcos L, Miller J, Poovathor S, Rubano J, Rutigliano D, Sands M, Santora C, Schwartz J, Shroyer K, Spitzer S, Stopeck A, Talamini M, Tharakan M, Vosswinkel J, Wertheim W. Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study. Open Forum Infect Dis 2020; 7:ofaa436. [PMID: 33117852 PMCID: PMC7543608 DOI: 10.1093/ofid/ofaa436] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 09/09/2020] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The global coronavirus disease 2019 (COVID-19) pandemic offers the opportunity to assess how hospitals manage the care of hospitalized patients with varying demographics and clinical presentations. The goal of this study was to demonstrate the impact of densely populated residential areas on hospitalization and to identify predictors of length of stay and mortality in hospitalized patients with COVID-19 in one of the hardest hit counties internationally. METHODS This was a single-center cohort study of 1325 sequentially hospitalized patients with COVID-19 in New York between March 2, 2020, to May 11, 2020. Geospatial distribution of study patients' residences relative to population density in the region were mapped, and data analysis included hospital length of stay, need and duration of invasive mechanical ventilation (IMV), and mortality. Logistic regression models were constructed to predict discharge dispositions in the remaining active study patients. RESULTS The median age of the study cohort (interquartile range [IQR]) was 62 (49-75) years, and more than half were male (57%) with history of hypertension (60%), obesity (41%), and diabetes (42%). Geographic residence of the study patients was disproportionately associated with areas of higher population density (r s = 0.235; P = .004), with noted "hot spots" in the region. Study patients were predominantly hypertensive (MAP > 90 mmHg; 670, 51%) on presentation with lymphopenia (590, 55%), hyponatremia (411, 31%), and kidney dysfunction (estimated glomerular filtration rate < 60 mL/min/1.73 m2; 381, 29%). Of the patients with a disposition (1188/1325), 15% (182/1188) required IMV and 21% (250/1188) developed acute kidney injury. In patients on IMV, the median (IQR) hospital length of stay in survivors (22 [16.5-29.5] days) was significantly longer than that of nonsurvivors (15 [10-23.75] days), but this was not due to prolonged time on the ventilator. The overall mortality in all hospitalized patients was 15%, and in patients receiving IMV it was 48%, which is predicted to minimally rise from 48% to 49% based on logistic regression models constructed to project disposition in the remaining patients on ventilators. Acute kidney injury during hospitalization (odds ratioE, 3.23) was the strongest predictor of mortality in patients requiring IMV. CONCLUSIONS This is the first study to collectively utilize the demographics, clinical characteristics, and hospital course of COVID-19 patients to identify predictors of poor outcomes that can be used for resource allocation in future waves of the pandemic.
Collapse
Affiliation(s)
| | - S K Mallipattu
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Jawa
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Moffitt
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Hajagos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - B Fries
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Nachman
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T J Gan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Kaushansky
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - H Skopicki
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Abell-Hart
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - I Chaudhri
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Deng
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Garcia
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Gayen
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T Kurc
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - O Bolotova
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Yoo
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Dhaliwal
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Nataraj
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Sun
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Tsai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - Y Wang
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Abbasi
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Abdullah
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Ahmad
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Bai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - E Bennett-Guerrero
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Chua
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Gomes
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Griffel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Kalogeropoulos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Kiamanesh
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Kim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - F Koraishy
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Lingham
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Mansour
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - L Marcos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Miller
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Poovathor
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Rubano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Rutigliano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Sands
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Santora
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Schwartz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Shroyer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Spitzer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Stopeck
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Talamini
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Tharakan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Vosswinkel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - W Wertheim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S K Mallipattu
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Jawa
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Moffitt
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Hajagos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - B Fries
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Nachman
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T J Gan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Kaushansky
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - H Skopicki
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Abell-Hart
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - I Chaudhri
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Deng
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Garcia
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Gayen
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T Kurc
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - O Bolotova
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Yoo
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Dhaliwal
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Nataraj
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Sun
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Tsai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - Y Wang
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Abbasi
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Abdullah
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Ahmad
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Bai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - E Bennett-Guerrero
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Chua
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Gomes
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Griffel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Kalogeropoulos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Kiamanesh
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Kim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - F Koraishy
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Lingham
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Mansour
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - L Marcos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Miller
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Poovathor
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Rubano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Rutigliano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Sands
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Santora
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Schwartz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Shroyer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Spitzer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Stopeck
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Talamini
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Tharakan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Vosswinkel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - W Wertheim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| |
Collapse
|
21
|
Fassler DJ, Abousamra S, Gupta R, Chen C, Zhao M, Paredes D, Batool SA, Knudsen BS, Escobar-Hoyos L, Shroyer KR, Samaras D, Kurc T, Saltz J. Publisher Correction to: Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images. Diagn Pathol 2020; 15:116. [PMID: 32972449 PMCID: PMC7513292 DOI: 10.1186/s13000-020-01021-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Danielle J Fassler
- Department of Pathology, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Shahira Abousamra
- Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Chao Chen
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Maozheng Zhao
- Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA
| | - David Paredes
- Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA
| | - Syeda Areeha Batool
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Beatrice S Knudsen
- Department of Pathology, University of Utah, 2000 Circle of Hope, Salt Lake City, UT, 84112, USA
| | - Luisa Escobar-Hoyos
- Department of Pathology, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA.,Department Therapeutic Radiology, Yale University, 15 York Street, New Haven, CT, 06513, USA
| | - Kenneth R Shroyer
- Department of Pathology, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA.
| |
Collapse
|
22
|
Fassler DJ, Abousamra S, Gupta R, Chen C, Zhao M, Paredes D, Batool SA, Knudsen BS, Escobar-Hoyos L, Shroyer KR, Samaras D, Kurc T, Saltz J. Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images. Diagn Pathol 2020; 15:100. [PMID: 32723384 PMCID: PMC7385962 DOI: 10.1186/s13000-020-01003-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 07/12/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of the unique colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel. METHODS Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and ensemble methods that employ both ColorAE and U-Net, collectively referred to as (3) ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor). RESULTS We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect 6 different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net into ensemble methods outperform using either ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME). We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also present a use case, wherein we apply the ColorAE:U-Net ensemble method across 3 mIHC WSIs and use the predictions to quantify all stained cell populations and perform nearest neighbor spatial analysis. Thus, we provide proof of concept that these methods can be employed to quantitatively describe the spatial distribution immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.
Collapse
Affiliation(s)
- Danielle J Fassler
- Department of Pathology, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Shahira Abousamra
- Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Chao Chen
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Maozheng Zhao
- Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA
| | - David Paredes
- Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA
| | - Syeda Areeha Batool
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Beatrice S Knudsen
- Department of Pathology, University of Utah, 2000 Circle of Hope, Salt Lake City, UT, 84112, USA
| | - Luisa Escobar-Hoyos
- Department of Pathology, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
- Department Therapeutic Radiology, Yale University, 15 York Street, New Haven, CT, 06513, USA
| | - Kenneth R Shroyer
- Department of Pathology, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA.
| |
Collapse
|
23
|
Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, Foran D, Do N, Golemati S, Kurc T, Huang K, Nikita KS, Veasey BP, Zervakis M, Saltz JH, Pattichis CS. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform 2020; 24:1837-1857. [PMID: 32609615 PMCID: PMC8580417 DOI: 10.1109/jbhi.2020.2991043] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.
Collapse
|
24
|
Le H, Gupta R, Hou L, Abousamra S, Fassler D, Torre-Healy L, Moffitt RA, Kurc T, Samaras D, Batiste R, Zhao T, Rao A, Van Dyke AL, Sharma A, Bremer E, Almeida JS, Saltz J. Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer. Am J Pathol 2020; 190:1491-1504. [PMID: 32277893 PMCID: PMC7369575 DOI: 10.1016/j.ajpath.2020.03.012] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 02/28/2020] [Accepted: 03/19/2020] [Indexed: 11/22/2022]
Abstract
Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network analysis pipelines to generate combined maps of cancer regions and TILs in routine diagnostic breast cancer whole slide tissue images. The combined maps provide insight about the structural patterns and spatial distribution of lymphocytic infiltrates and facilitate improved quantification of TILs. Both tumor and TIL analyses were evaluated by using three convolutional neural network networks (34-layer ResNet, 16-layer VGG, and Inception v4); the results compared favorably with those obtained by using the best published methods. We have produced open-source tools and a public data set consisting of tumor/TIL maps for 1090 invasive breast cancer images from The Cancer Genome Atlas. The maps can be downloaded for further downstream analyses.
Collapse
Affiliation(s)
- Han Le
- Department of Computer Science, Stony Brook University, Stony Brook, New York.
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York; Department of Pathology, Stony Brook University Hospital, Stony Brook, New York
| | - Le Hou
- Department of Computer Science, Stony Brook University, Stony Brook, New York
| | - Shahira Abousamra
- Department of Computer Science, Stony Brook University, Stony Brook, New York
| | - Danielle Fassler
- Department of Pathology, Stony Brook University Hospital, Stony Brook, New York
| | - Luke Torre-Healy
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York; Department of Pathology, Stony Brook University Hospital, Stony Brook, New York
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, New York
| | - Rebecca Batiste
- Department of Pathology, Stony Brook University Hospital, Stony Brook, New York
| | - Tianhao Zhao
- Department of Pathology, Stony Brook University Hospital, Stony Brook, New York
| | - Arvind Rao
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan
| | - Alison L Van Dyke
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
| | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York
| | - Jonas S Almeida
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York
| |
Collapse
|
25
|
Moore M, Friesner ID, Rizk EM, Trager M, Celebi JT, Rich J, Chikeka I, Kurc T, Wang J, Rohr B, Robinson E, Geskin LJ, Horst B, Gardner K, Niedt G, Messina J, Ferringer T, Saltz JH, Vanguri R, Saenger YM. Effect of automated TIL quantification in early-stage melanoma on accuracy of standard T staging using AJCC guidelines. J Clin Oncol 2020. [DOI: 10.1200/jco.2020.38.15_suppl.10076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
10076 Background: Patients diagnosed with early stage melanoma are at risk of recurrence and death. Adjuvant therapy decreases risk but incurs toxicity and expense. While tumor-infiltrating lymphocytes (TILs) improve prognosis, studies have shown conflicting results due, at least in part, to inter-observer variability. Thus, TILs are not included in standard American Joint Committee on Cancer (AJCC) staging. Here, we quantitatively analyze TILs in hematoxylin and eosin (H&E) melanoma images using two machine learning algorithms. Methods: H&E images were evaluated by two methods for patients with resectable stage I-III melanoma from Columbia (N = 81) and validated using samples from Geisinger and Moffitt (N = 128). For both methods, H&E images were manually annotated using open source software, QuPath, to specify tumor regions. For Method A, images were divided into patches and, for each patch, a probability was generated to detect lymphocytes. Patches above a set threshold were considered to be “TIL positive”. Ratio of TIL positive patches to total patches was assessed for every image. For Method B, a classifier was manually trained in QuPath and then applied on each image to determine the ratio of the areas of all immune cells to all tumor cells as previously published. Cutoff values to define high and low risk groups were established based on a test set and then validated in an independent cohort. Results: Both methods distinguished patients with visceral recurrence from those without for the Columbia training set (Method A p = .0015, Method B p = .043). Using Method A, Kaplan-Meier curve at the selected cutoff also correlated significantly with disease specific survival (DSS) for Columbia (p = .022) and was validated in the Geisinger/Moffitt (p = .046) cohort. Cox analysis using Method A showed that TIL status predicted DSS in the validation set (p = .047) and added significantly to depth and ulceration (HR = 3.43, CI: 1.047-11.257, p = .042). Conclusions: Both open source machine learning algorithms find significantly higher TILs in patients who do not develop metastasis. Notably, Method A may add to standard predictors, such as depth and ulceration. These results demonstrate the promise of computational algorithms to enhance visual grading, and suggest that digital TIL evaluation may add to current AJCC staging. [Table: see text]
Collapse
Affiliation(s)
| | | | | | - Megan Trager
- Vagelos College of Physicians and Surgeons, New York, NY
| | | | - Jeani Rich
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | | | | | - Jing Wang
- New York University Department of Anesthesiology, Perioperative Care and Pain Medicine, New York, NY
| | - Bethany Rohr
- Department of Pathology, Geisinger Health Systems, Danville, PA
| | - Eric Robinson
- New York University Department of Anaesthesia, New York, NY
| | | | - Basil Horst
- Vancouver General Hospital, University of British Columbia, Vancouver, BC, Canada
| | | | - George Niedt
- Department of Dermatology, Columbia University Medical Center, New York, NY
| | - Jane Messina
- H. Lee. Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Tammy Ferringer
- Geisinger Health system Department of Pathology, New York, NY
| | | | - Rami Vanguri
- Department of Pathology and Cell Biology, Columbia University Irvine Medical Center, New York, NY
| | | |
Collapse
|
26
|
Kurc T, Bakas S, Ren X, Bagari A, Momeni A, Huang Y, Zhang L, Kumar A, Thibault M, Qi Q, Wang Q, Kori A, Gevaert O, Zhang Y, Shen D, Khened M, Ding X, Krishnamurthi G, Kalpathy-Cramer J, Davis J, Zhao T, Gupta R, Saltz J, Farahani K. Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches. Front Neurosci 2020; 14:27. [PMID: 32153349 PMCID: PMC7046596 DOI: 10.3389/fnins.2020.00027] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 01/10/2020] [Indexed: 12/12/2022] Open
Abstract
Biomedical imaging Is an important source of information in cancer research. Characterizations of cancer morphology at onset, progression, and in response to treatment provide complementary information to that gleaned from genomics and clinical data. Accurate extraction and classification of both visual and latent image features Is an increasingly complex challenge due to the increased complexity and resolution of biomedical image data. In this paper, we present four deep learning-based image analysis methods from the Computational Precision Medicine (CPM) satellite event of the 21st International Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) conference. One method Is a segmentation method designed to segment nuclei in whole slide tissue images (WSIs) of adult diffuse glioma cases. It achieved a Dice similarity coefficient of 0.868 with the CPM challenge datasets. Three methods are classification methods developed to categorize adult diffuse glioma cases into oligodendroglioma and astrocytoma classes using radiographic and histologic image data. These methods achieved accuracy values of 0.75, 0.80, and 0.90, measured as the ratio of the number of correct classifications to the number of total cases, with the challenge datasets. The evaluations of the four methods indicate that (1) carefully constructed deep learning algorithms are able to produce high accuracy in the analysis of biomedical image data and (2) the combination of radiographic with histologic image information improves classification performance.
Collapse
Affiliation(s)
- Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Xuhua Ren
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Aditya Bagari
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India
| | - Alexandre Momeni
- Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Yue Huang
- School of Informatics, Xiamen University, Xiamen, China
| | - Lichi Zhang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ashish Kumar
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India
| | - Marc Thibault
- Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Qi Qi
- School of Informatics, Xiamen University, Xiamen, China
| | - Qian Wang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Avinash Kori
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India
| | - Olivier Gevaert
- Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Yunlong Zhang
- School of Informatics, Xiamen University, Xiamen, China
| | - Dinggang Shen
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Mahendra Khened
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India
| | - Xinghao Ding
- School of Informatics, Xiamen University, Xiamen, China
| | | | - Jayashree Kalpathy-Cramer
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - James Davis
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Tianhao Zhao
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| |
Collapse
|
27
|
Barreiros W, Moreira J, Kurc T, Kong J, Melo AC, Saltz JH, Teodoro G. Optimizing parameter sensitivity analysis of large-scale microscopy image analysis workflows with multilevel computation reuse. Concurr Comput 2020; 32:e5403. [PMID: 32669980 PMCID: PMC7363336 DOI: 10.1002/cpe.5403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 05/18/2019] [Indexed: 06/11/2023]
Abstract
Parameter sensitivity analysis (SA) is an effective tool to gain knowledge about complex analysis applications and assess the variability in their analysis results. However, it is an expensive process as it requires the execution of the target application multiple times with a large number of different input parameter values. In this work, we propose optimizations to reduce the overall computation cost of SA in the context of analysis applications that segment high-resolution slide tissue images, ie, images with resolutions of 100k × 100k pixels. Two cost-cutting techniques are combined to efficiently execute SA: use of distributed hybrid systems for parallel execution and computation reuse at multiple levels of an analysis pipeline to reduce the amount of computation. These techniques were evaluated using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. Our parallel execution method attained an efficiency of over 90% on 256 nodes. The hybrid execution on the CPU and Intel Phi improved the performance by 2×. Multilevel computation reuse led to performance gains of over 2.9×.
Collapse
Affiliation(s)
- Willian Barreiros
- Department of Computer Science, University of Brasília, Brasília, Brazil
| | - Jeremias Moreira
- Department of Computer Science, University of Brasília, Brasília, Brazil
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Jun Kong
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
- Department of Computer Science, Emory University, Atlanta, Georgia
- Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia
| | - Alba C.M.A. Melo
- Department of Computer Science, University of Brasília, Brasília, Brazil
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - George Teodoro
- Department of Computer Science, University of Brasília, Brasília, Brazil
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Brazil
| |
Collapse
|
28
|
Prior F, Almeida J, Kathiravelu P, Kurc T, Smith K, Fitzgerald TJ, Saltz J. Open access image repositories: high-quality data to enable machine learning research. Clin Radiol 2020; 75:7-12. [PMID: 31040006 PMCID: PMC6815686 DOI: 10.1016/j.crad.2019.04.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 04/01/2019] [Indexed: 02/07/2023]
Abstract
Originally motivated by the need for research reproducibility and data reuse, large-scale, open access information repositories have become key resources for training and testing of advanced machine learning applications in biomedical and clinical research. To be of value, such repositories must provide large, high-quality data sets, where quality is defined as minimising variance due to data collection protocols and data misrepresentations. Curation is the key to quality. We have constructed a large public access image repository, The Cancer Imaging Archive, dedicated to the promotion of open science to advance the global effort to diagnose and treat cancer. Drawing on this experience and our experience in applying machine learning techniques to the analysis of radiology and pathology image data, we will review the requirements placed on such information repositories by state-of-the-art machine learning applications and how these requirements can be met.
Collapse
Affiliation(s)
- F Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR 72205, USA.
| | - J Almeida
- National Institutes of Health, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
| | - P Kathiravelu
- Department of Biomedical Informatics, Emory University, 101 Woodruff Circle, #4104, Atlanta, GA 30322, USA
| | - T Kurc
- Department of Biomedical Informatics, Stoney Brook University, Health Science Center Level 3, Room 043, Stony Brook, NY 11794, USA
| | - K Smith
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR 72205, USA
| | - T J Fitzgerald
- Department of Radiation Oncology, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - J Saltz
- Department of Biomedical Informatics, Stoney Brook University, Health Science Center Level 3, Room 043, Stony Brook, NY 11794, USA
| |
Collapse
|
29
|
Kobayashi S, Le H, Chrastecka L, Gupta R, Hou L, Abousamra S, Fassler D, Shroyer KR, Samaras D, Kurc T, Moffitt RA, Saltz JH. Abstract A27: Deep learning for analysis of tumor-lymphocyte interactions in pancreatic ductal adenocarcinoma. Cancer Res 2019. [DOI: 10.1158/1538-7445.panca19-a27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
The interaction of tumor, stroma, and immune cells in pancreatic ductal adenocarcinoma (PDAC) is complex and difficult to quantify in patient samples. Recently, deep learning algorithms have shown successes in identifying tumor and lymphocytes regions on whole-slide images derived from routinely collected histopathologic specimens. The Cancer Genome Atlas (TCGA) in particular has generated whole-slide images as well as paired molecular data, thus allowing for combined spatial and molecular analyses of tumor-lymphocyte interactions. We have previously highlighted this resource by computationally mapping tumor-infiltrating lymphocytes (TILs) on digital images across 13 tumor types. To achieve this, convolutional neural networks were trained on lymphocyte images annotated by expert pathologists and then used to detect spatial TIL patterns. This led to identification of four qualitative TIL pattern categories, which varied depending on tumor type as well as molecular immune subtype, demonstrating the potential of these spatial structures to provide further insights into tumor microenvironments and their relationship to overall survival. We have now extended this deep learning pipeline to include identification of tumor regions in PDAC, allowing study of TIL patterns in the context of their relative spatial localization to tumors. Using the deep learning algorithm to define the tumor region, we applied erosion and dilation operations to further capture the peritumoral region, the outer and inner regions of the tumor, as well as desmoplasia far from the tumor cells. We thus defined lymphocytes by their spatial localization as being internal, tumoral, peritumoral, or outer with these masks. We then used nearest-neighbor and density-based approaches to quantify TIL infiltration patterns with respect to tumor. These features vary significantly across the previously identified TIL patterns and may serve as additional parameters to define the microenvironment conditions in patient samples. Here we demonstrate that features extracted using our pipeline recapitulate canonical histologic properties. Using immune cell abundance estimates from gene expression generated by CIBERSORT, we find that samples with tumor TIL densities above median have more M1 macrophages, while those below median have more M2 macrophages. We also observe that slides with a higher peritumoral TIL density relative to tumoral TIL density have higher Treg fractions. Ongoing work on improving the resolution and cell specificity of our pipeline will allow us to ask more specific questions and permit higher granularity in linking clinical outcomes to spatial immune phenotypes.
Citation Format: Soma Kobayashi, Han Le, Lucie Chrastecka, Rajarsi Gupta, Le Hou, Shahira Abousamra, Danielle Fassler, Kenneth R. Shroyer, Dimitris Samaras, Tahsin Kurc, Richard A. Moffitt, Joel H. Saltz. Deep learning for analysis of tumor-lymphocyte interactions in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr A27.
Collapse
Affiliation(s)
| | - Han Le
- Stony Brook University, Stony Brook, NY
| | | | | | - Le Hou
- Stony Brook University, Stony Brook, NY
| | | | | | | | | | | | | | | |
Collapse
|
30
|
|
31
|
Vu QD, Graham S, Kurc T, To MNN, Shaban M, Qaiser T, Koohbanani NA, Khurram SA, Kalpathy-Cramer J, Zhao T, Gupta R, Kwak JT, Rajpoot N, Saltz J, Farahani K. Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Front Bioeng Biotechnol 2019; 7:53. [PMID: 31001524 PMCID: PMC6454006 DOI: 10.3389/fbioe.2019.00053] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 03/01/2019] [Indexed: 12/12/2022] Open
Abstract
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.
Collapse
Affiliation(s)
- Quoc Dang Vu
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Simon Graham
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Minh Nguyen Nhat To
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Muhammad Shaban
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Talha Qaiser
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | | | - Syed Ali Khurram
- School of Clinical Dentistry, The University of Sheffield, Sheffield, United Kingdom
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Harvard Medical School and Mass General Hospital, Boston, MA, United States
| | - Tianhao Zhao
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Jin Tae Kwak
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Nasir Rajpoot
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| |
Collapse
|
32
|
Gomes J, Barreiros W, Kurc T, Melo ACMA, Kong J, Saltz JH, Teodoro G. Sensitivity analysis in digital pathology: Handling large number of parameters with compute expensive workflows. Comput Biol Med 2019; 108:371-381. [PMID: 31054503 DOI: 10.1016/j.compbiomed.2019.03.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 02/28/2019] [Accepted: 03/07/2019] [Indexed: 12/19/2022]
Abstract
Digital pathology imaging enables valuable quantitative characterizations of tissue state at the sub-cellular level. While there is a growing set of methods for analysis of whole slide tissue images, many of them are sensitive to changes in input parameters. Evaluating how analysis results are affected by variations in input parameters is important for the development of robust methods. Executing algorithm sensitivity analyses by systematically varying input parameters is an expensive task because a single evaluation run with a moderate number of tissue images may take hours or days. Our work investigates the use of Surrogate Models (SMs) along with parallel execution to speed up parameter sensitivity analysis (SA). This approach significantly reduces the SA cost, because the SM execution is inexpensive. The evaluation of several SM strategies with two image segmentation workflows demonstrates that a SA study with SMs attains results close to a SA with real application runs (mean absolute error lower than 0.022), while the SM accelerates the SA execution by 51 × . We also show that, although the number of parameters in the example workflows is high, most of the uncertainty can be associated with a few parameters. In order to identify the impact of variations in segmentation results to downstream analyses, we carried out a survival analysis with 387 Lung Squamous Cell Carcinoma cases. This analysis was repeated using 3 values for the most significant parameters identified by the SA for the two segmentation algorithms; about 600 million cell nuclei were segmented per run. The results show that significance of the survival correlations of patient groups, assessed by a logrank test, are strongly affected by the segmentation parameter changes. This indicates that sensitivity analysis is an important tool for evaluating the stability of conclusions from image analyses.
Collapse
Affiliation(s)
- Jeremias Gomes
- Department of Computer Science, University of Brasília, Brazil
| | | | - Tahsin Kurc
- Biomedical Informatics Department, Stony Brook University, Stony Brook, USA; Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, USA
| | - Alba C M A Melo
- Department of Computer Science, University of Brasília, Brazil
| | - Jun Kong
- Biomedical Informatics Department, Emory University, Atlanta, USA; Department of Biomedical Engineering, Emory-Georgia Institute of Technology, Atlanta, USA; Department of Mathematics and Statistics, Georgia State University, Atlanta, USA
| | - Joel H Saltz
- Biomedical Informatics Department, Stony Brook University, Stony Brook, USA
| | - George Teodoro
- Department of Computer Science, University of Brasília, Brazil; Biomedical Informatics Department, Stony Brook University, Stony Brook, USA.
| |
Collapse
|
33
|
Pantanowitz L, Sharma A, Carter AB, Kurc T, Sussman A, Saltz J. Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. J Pathol Inform 2018; 9:40. [PMID: 30607307 PMCID: PMC6289005 DOI: 10.4103/jpi.jpi_69_18] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 10/28/2018] [Indexed: 12/13/2022] Open
Abstract
Almost 20 years have passed since the commercial introduction of whole-slide imaging (WSI) scanners. During this time, the creation of various WSI devices with the ability to digitize an entire glass slide has transformed the field of pathology. Parallel advances in computational technology and storage have permitted rapid processing of large-scale WSI datasets. This article provides an overview of important past and present efforts related to WSI. An account of how the virtual microscope evolved from the need to visualize and manage satellite data for earth science applications is provided. The article also discusses important milestones beginning from the first WSI scanner designed by Bacus to the Food and Drug Administration approval of the first digital pathology system for primary diagnosis in surgical pathology. As pathology laboratories commit to going fully digitalize, the need has emerged to include WSIs into an enterprise-level vendor-neutral archive (VNA). The different types of VNAs available are reviewed as well as how best to implement them and how pathology can benefit from participating in this effort. Differences between traditional image algorithms that extract pixel-, object-, and semantic-level features versus deep learning methods are highlighted. The need for large-scale data management, analysis, and visualization in computational pathology is also addressed.
Collapse
Affiliation(s)
- Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, GA, USA
| | - Alexis B. Carter
- Department of Pathology and Laboratory Medicine, Children's Healthcare of Atlanta, GA, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Alan Sussman
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
34
|
Gomes J, de Melo ACMA, Kong J, Kurc T, Saltz JH, Teodoro G. Cooperative and out-of-core execution of the irregular wavefront propagation pattern on hybrid machines with Intel Ⓡ Xeon Phi™. Concurr Comput 2018; 30:e4425. [PMID: 30344454 PMCID: PMC6195363 DOI: 10.1002/cpe.4425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The Irregular Wavefront Propagation Pattern (IWPP) is a core computing structure in several image analysis operations. Efficient implementation of IWPP on the Intel Xeon Phi is difficult because of the irregular data access and computation characteristics. The traditional IWPP algorithm relies on atomic instructions, which are not available in the SIMD set of the Intel Phi. To overcome this limitation, we have proposed a new IWPP algorithm that can take advantage of non-atomic SIMD instructions supported on the Intel Xeon Phi. We have also developed and evaluated methods to use CPU and Intel Phi cooperatively for parallel execution of the IWPP algorithms. Our new cooperative IWPP version is also able to handle large out-of-core images that would not fit into the memory of the accelerator. The new IWPP algorithm is used to implement the Morphological Reconstruction and Fill Holes operations, which are operations commonly found in image analysis applications. The vectorization implemented with the new IWPP has attained improvements of up to about 5× on top of the original IWPP and significant gains as compared to state-of-the-art the CPU and GPU versions. The new version running on an Intel Phi is 6.21× and 3.14× faster than running on a 16-core CPU and on a GPU, respectively. Finally, the cooperative execution using two Intel Phi devices and a multi-core CPU has reached performance gains of 2.14× as compared to the execution using a single Intel Xeon Phi.
Collapse
Affiliation(s)
- Jeremias Gomes
- Department of Computer Science, University of Brasília, Brasília-DF, Brazil
| | | | - Jun Kong
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - George Teodoro
- Department of Computer Science, University of Brasília, Brasília-DF, Brazil
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
35
|
Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Shmulevich I, Rao AUK, Lazar AJ, Sharma A, Thorsson V. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep 2018; 23:181-193.e7. [PMID: 29617659 PMCID: PMC5943714 DOI: 10.1016/j.celrep.2018.03.086] [Citation(s) in RCA: 498] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 02/27/2018] [Accepted: 03/20/2018] [Indexed: 02/07/2023] Open
Abstract
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.
Collapse
Affiliation(s)
- Joel Saltz
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY 11794, USA.
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY 11794, USA; Department of Pathology, Stony Brook Medicine, Stony Brook, NY 11794, USA
| | - Le Hou
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY 11794, USA
| | - Pankaj Singh
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Vu Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
| | - Kenneth R Shroyer
- Department of Pathology, Stony Brook Medicine, Stony Brook, NY 11794, USA
| | - Tianhao Zhao
- Department of Pathology, Stony Brook Medicine, Stony Brook, NY 11794, USA
| | - Rebecca Batiste
- Department of Pathology, Stony Brook Medicine, Stony Brook, NY 11794, USA
| | - John Van Arnam
- Department of Pathology and Laboratory Medicine, Perelman School at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Arvind U K Rao
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Alexander J Lazar
- Departments of Pathology, Genomic Medicine, and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
| | | |
Collapse
|
36
|
Saltz J, Sharma A, Iyer G, Bremer E, Wang F, Jasniewski A, DiPrima T, Almeida JS, Gao Y, Zhao T, Saltz M, Kurc T. A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images. Cancer Res 2017; 77:e79-e82. [PMID: 29092946 DOI: 10.1158/0008-5472.can-17-0316] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 06/17/2017] [Accepted: 09/01/2017] [Indexed: 11/16/2022]
Abstract
Well-curated sets of pathology image features will be critical to clinical studies that aim to evaluate and predict treatment responses. Researchers require information synthesized across multiple biological scales, from the patient to the molecular scale, to more effectively study cancer. This article describes a suite of services and web applications that allow users to select regions of interest in whole slide tissue images, run a segmentation pipeline on the selected regions to extract nuclei and compute shape, size, intensity, and texture features, store and index images and analysis results, and visualize and explore images and computed features. All the services are deployed as containers and the user-facing interfaces as web-based applications. The set of containers and web applications presented in this article is used in cancer research studies of morphologic characteristics of tumor tissues. The software is free and open source. Cancer Res; 77(21); e79-82. ©2017 AACR.
Collapse
Affiliation(s)
- Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
| | - Ganesh Iyer
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
| | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Feiqiao Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Alina Jasniewski
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Tammy DiPrima
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Jonas S Almeida
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Yi Gao
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Tianhao Zhao
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.,Department of Pathology, Stony Brook University, Stony Brook, New York
| | - Mary Saltz
- Department of Radiology, Stony Brook University, Stony Brook, New York
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.,Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| |
Collapse
|
37
|
Baig F, Vo H, Kurc T, Saltz J, Wang F. SparkGIS: Resource Aware Efficient In-Memory Spatial Query Processing. Proc ACM SIGSPATIAL Int Conf Adv Inf 2017; 2017:28. [PMID: 30035278 PMCID: PMC6054321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Much effort has been devoted to support high performance spatial queries on large volumes of spatial data in distributed spatial computing systems, especially in the MapReduce paradigm. Recent works have focused on extending spatial MapReduce frameworks to leverage high performance in-memory distributed processing capabilities of systems such as Spark. However, the performance advantage comes with the requirement of having enough memory and comprehensive configuration. Failing to fulfill this falls back to disk IO, defeating the purpose of such systems or in worst case gets out of memory and fails the job. The problem is aggravated further for spatial processing since the underlying in-memory systems are oblivious of spatial data features and characteristics. In this paper we present SparkGIS - an in-memory oriented spatial data querying system for high throughput and low latency spatial query handling by adapting Apache Spark's distributed processing capabilities. It supports basic spatial queries including containment, spatial join and k-nearest neighbor and allows extending these to complex query pipelines. SparkGIS mitigates skew in distributed processing by supporting several dynamic partitioning algorithms suitable for a rich set of contemporary application scenarios. Multilevel global and local, pre-generated and on-demand in-memory indexes, allow SparkGIS to prune input data and apply compute intensive operations on a subset of relevant spatial objects only. Finally, SparkGIS employs dynamic query rewriting to gracefully manage large spatial query workflows that exceed available distributed resources. Our comparative evaluation has shown that the performance of SparkGIS is on par with contemporary Spark based platforms for relatively smaller queries and outperforms them for larger data and memory intensive workflows by dynamic query rewriting and efficient spatial data management.
Collapse
|
38
|
Barreiros W, Teodoro G, Kurc T, Kong J, Melo ACMA, Saltz J. Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems. Proc IEEE Int Conf Clust Comput 2017; 2017:25-35. [PMID: 29081725 DOI: 10.1109/cluster.2017.28] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2×. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46× on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.
Collapse
Affiliation(s)
- Willian Barreiros
- Department of Computer Science, University of Brasília, Brasília, DF, Brazil
| | - George Teodoro
- Department of Computer Science, University of Brasília, Brasília, DF, Brazil.,Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.,Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jun Kong
- Biomedical Informatics Department, Emory University, Atlanta, GA, USA
| | - Alba C M A Melo
- Department of Computer Science, University of Brasília, Brasília, DF, Brazil
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
39
|
Saltz J, Almeida J, Gao Y, Sharma A, Bremer E, DiPrima T, Saltz M, Kalpathy-Cramer J, Kurc T. Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research. AMIA Jt Summits Transl Sci Proc 2017; 2017:85-94. [PMID: 28815113 PMCID: PMC5543366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cancer is a complex multifactorial disease state and the ability to anticipate and steer treatment results will require information synthesis across multiple scales from the host to the molecular level. Radiomics and Pathomics, where image features are extracted from routine diagnostic Radiology and Pathology studies, are also evolving as valuable diagnostic and prognostic indicators in cancer. This information explosion provides new opportunities for integrated, multi-scale investigation of cancer, but also mandates a need to build systematic and integrated approaches to manage, query and mine combined Radiomics and Pathomics data. In this paper, we describe a suite of tools and web-based applications towards building a comprehensive framework to support the generation, management and interrogation of large volumes of Radiomics and Pathomics feature sets and the investigation of correlations between image features, molecular data, and clinical outcome.
Collapse
Affiliation(s)
- Joel Saltz
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Jonas Almeida
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Yi Gao
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Ashish Sharma
- Biomedical Informatics Department, Emory University, Atlanta, GA
| | - Erich Bremer
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Tammy DiPrima
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Mary Saltz
- Department of Radiology, Stony Brook University, Stony Brook, NY
| | | | - Tahsin Kurc
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN
| |
Collapse
|
40
|
Zhou N, Yu X, Zhao T, Wen S, Wang F, Zhu W, Kurc T, Tannenbaum A, Saltz J, Gao Y. Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis. Proc SPIE Int Soc Opt Eng 2017; 10140. [PMID: 30344361 DOI: 10.1117/12.2254220] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Digital histopathology images with more than 1 Gigapixel are drawing more and more attention in clinical, biomedical research, and computer vision fields. Among the multiple observable features spanning multiple scales in the pathology images, the nuclear morphology is one of the central criteria for diagnosis and grading. As a result it is also the mostly studied target in image computing. Large amount of research papers have devoted to the problem of extracting nuclei from digital pathology images, which is the foundation of any further correlation study. However, the validation and evaluation of nucleus extraction have yet been formulated rigorously and systematically. Some researches report a human verified segmentation with thousands of nuclei, whereas a single whole slide image may contain up to million. The main obstacle lies in the difficulty of obtaining such a large number of validated nuclei, which is essentially an impossible task for pathologist. We propose a systematic validation and evaluation approach based on large scale image synthesis. This could facilitate a more quantitatively validated study for current and future histopathology image analysis field.
Collapse
Affiliation(s)
- Naiyun Zhou
- Department of Biomedical Engineering, Stony Brook University
| | - Xiaxia Yu
- Department of Biomedical Informatics, Stony Brook University
| | - Tianhao Zhao
- Department of Biomedical Informatics, Stony Brook University
| | - Si Wen
- Department of Applied Mathematics and Statistics, Stony Brook University
| | - Fusheng Wang
- Department of Biomedical Informatics, Stony Brook University.,Department of Computer Science, Stony Brook University
| | - Wei Zhu
- Department of Applied Mathematics and Statistics, Stony Brook University
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University.,Department of Computer Science, Stony Brook University
| | - Allen Tannenbaum
- Department of Computer Science, Stony Brook University.,Department of Applied Mathematics and Statistics, Stony Brook University
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University.,Department of Computer Science, Stony Brook University
| | - Yi Gao
- Department of Biomedical Informatics, Stony Brook University.,Department of Applied Mathematics and Statistics, Stony Brook University
| |
Collapse
|
41
|
Bremer E, Kurc T, Gao Y, Saltz J, Almeida JS. Safe "cloudification" of large images through picker APIs. AMIA Annu Symp Proc 2017; 2016:342-351. [PMID: 28269829 PMCID: PMC5333212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The "Box model" allows users with no particular training in informatics, or access to specialized infrastructure, operate generic cloud computing resources through a temporary URI dereferencing mechanism known as "drop-file-picker API" ("picker API" for sort). This application programming interface (API) was popularized in the web app development community by DropBox, and is now a consumer-facing feature of all major cloud computing platforms such as Box.com, Google Drive and Amazon S3. This reports describes a prototype web service application that uses picker APIs to expose a new, "cloudified", API tailored for image analysis, without compromising the private governance of the data exposed. In order to better understand this cross-platform cloud computing landscape, we first measured the time for both transfer and traversing of large image files generated by whole slide imaging (WSI) in Digital Pathology. The verification that there is extensive interconnectivity between cloud resources let to the development of a prototype software application that exposes an image-traversing REST API to image files stored in any of the consumer-facing "boxes". In summary, an image file can be upload/synchronized into a any cloud resource with a file picker API and the prototype service described here will expose an HTTP REST API that remains within the safety of the user's own governance. The open source prototype is publicly available at sbu-bmi.github.io/imagebox. Availability The accompanying prototype application is made publicly available, fully functional, with open source, at http://sbu-bmi.github.io/imagebox://sbu-bmi.github.io/imagebox. An illustrative webcasted use of this Web App is included with the project codebase at https://github.com/SBU-BMI/imageboxs://github.com/SBU-BMI/imagebox.
Collapse
Affiliation(s)
- Erich Bremer
- Dept Biomedical Informatics, Stony Brook University (SUNY), NY 11794
| | - Tahsin Kurc
- Dept Biomedical Informatics, Stony Brook University (SUNY), NY 11794
| | - Yi Gao
- Dept Biomedical Informatics, Stony Brook University (SUNY), NY 11794
| | - Joel Saltz
- Dept Biomedical Informatics, Stony Brook University (SUNY), NY 11794
| | - Jonas S Almeida
- Dept Biomedical Informatics, Stony Brook University (SUNY), NY 11794
| |
Collapse
|
42
|
Teodoro G, Kurc T, Andrade G, Kong J, Ferreira R, Saltz J. Application Performance Analysis and Efficient Execution on Systems with multi-core CPUs, GPUs and MICs: A Case Study with Microscopy Image Analysis. Int J High Perform Comput Appl 2017; 31:32-51. [PMID: 28239253 PMCID: PMC5319667 DOI: 10.1177/1094342015594519] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
We carry out a comparative performance study of multi-core CPUs, GPUs and Intel Xeon Phi (Many Integrated Core-MIC) with a microscopy image analysis application. We experimentally evaluate the performance of computing devices on core operations of the application. We correlate the observed performance with the characteristics of computing devices and data access patterns, computation complexities, and parallelization forms of the operations. The results show a significant variability in the performance of operations with respect to the device used. The performances of operations with regular data access are comparable or sometimes better on a MIC than that on a GPU. GPUs are more efficient than MICs for operations that access data irregularly, because of the lower bandwidth of the MIC for random data accesses. We propose new performance-aware scheduling strategies that consider variabilities in operation speedups. Our scheduling strategies significantly improve application performance compared to classic strategies in hybrid configurations.
Collapse
Affiliation(s)
- George Teodoro
- Department of Computer Science, University of Brasília, Brasília, DF, Brazil
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Guilherme Andrade
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Jun Kong
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
| | - Renato Ferreira
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| |
Collapse
|
43
|
Baig F, Mehrotra M, Vo H, Wang F, Saltz J, Kurc T. SparkGIS: Efficient Comparison and Evaluation of Algorithm Results in Tissue Image Analysis Studies. Biomed Data Manag Graph Online Querying (2015) 2016; 9579:134-146. [PMID: 30198025 PMCID: PMC6126541 DOI: 10.1007/978-3-319-41576-5_10] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison of multiple results, and facilitate algorithm sensitivity studies. The sizes of images and analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present SparkGIS, a distributed, in-memory spatial data processing framework to query, retrieve, and compare large volumes of analytical image result data for algorithm evaluation. Our approach combines the in-memory distributed processing capabilities of Apache Spark and the efficient spatial query processing of Hadoop-GIS. The experimental evaluation of SparkGIS for heatmap computations used to compare nucleus segmentation results from multiple images and analysis runs shows that SparkGIS is efficient and scalable, enabling algorithm evaluation and algorithm sensitivity studies on large datasets.
Collapse
Affiliation(s)
- Furqan Baig
- Department of Computer Science, Stony Brook University, New York, USA
| | - Mudit Mehrotra
- Department of Computer Science, Stony Brook University, New York, USA
| | - Hoang Vo
- Department of Computer Science, Stony Brook University, New York, USA
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, New York, USA
- Department of Biomedical Informatics, Stony Brook University, New York, USA
| | - Joel Saltz
- Department of Computer Science, Stony Brook University, New York, USA
- Department of Biomedical Informatics, Stony Brook University, New York, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, New York, USA
| |
Collapse
|
44
|
Gao Y, Ratner V, Zhu L, Diprima T, Kurc T, Tannenbaum A, Saltz J. Hierarchical nucleus segmentation in digital pathology images. Proc SPIE Int Soc Opt Eng 2016; 9791. [PMID: 27375315 DOI: 10.1117/12.2217029] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set.
Collapse
Affiliation(s)
- Yi Gao
- Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A; Department of Applied Mathematics & Statistics, Stony Brook University, NY, U.S.A
| | - Vadim Ratner
- Department of Computer Science, Stony Brook University, NY, U.S.A
| | - Liangjia Zhu
- Department of Computer Science, Stony Brook University, NY, U.S.A
| | - Tammy Diprima
- Department of Biomedical Informatics, Stony Brook University, NY, U.S.A
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A
| | - Allen Tannenbaum
- Department of Computer Science, Stony Brook University, NY, U.S.A; Department of Applied Mathematics & Statistics, Stony Brook University, NY, U.S.A
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A
| |
Collapse
|
45
|
Gao Y, Liu W, Arjun S, Zhu L, Ratner V, Kurc T, Saltz J, Tannenbaum A. Multi-scale learning based segmentation of glands in digital colonrectal pathology images. Proc SPIE Int Soc Opt Eng 2016; 9791:97910M. [PMID: 27818565 PMCID: PMC5091801 DOI: 10.1117/12.2216790] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation. After the learning step, the dictionaries are used collectively to perform the classification and segmentation for the new image.
Collapse
Affiliation(s)
- Yi Gao
- Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Applied Mathematics & Statistics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A
| | - William Liu
- The Upper School of Buckingham Browne & Nichols, MA, U.S.A
| | - Shipra Arjun
- Department of Biomedical Engineering, Stony Brook University, NY, U.S.A
| | - Liangjia Zhu
- Department of Biomedical Informatics, Stony Brook University, NY, U.S.A
| | - Vadim Ratner
- Department of Biomedical Informatics, Stony Brook University, NY, U.S.A
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A
| | - Allen Tannenbaum
- Department of Applied Mathematics & Statistics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A; Department of Biomedical Informatics, Stony Brook University, NY, U.S.A
| |
Collapse
|
46
|
Kurc T, Qi X, Wang D, Wang F, Teodoro G, Cooper L, Nalisnik M, Yang L, Saltz J, Foran DJ. Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies. BMC Bioinformatics 2015; 16:399. [PMID: 26627175 PMCID: PMC4667532 DOI: 10.1186/s12859-015-0831-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 11/16/2015] [Indexed: 11/16/2022] Open
Abstract
Background We describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands. Results The proposed tools and methods take advantage of state-of-the-art parallel machines and efficient content-based image searching strategies. The content based image retrieval (CBIR) algorithms can quickly detect and retrieve image patches similar to a query patch using a hierarchical analysis approach. The analysis component based on high performance computing can carry out consensus clustering on 500,000 data points using a large shared memory system. Conclusions Our work demonstrates efficient CBIR algorithms and high performance computing can be leveraged for efficient analysis of large microscopy images to meet the challenges of clinically salient applications in pathology. These technologies enable researchers and clinical investigators to make more effective use of the rich informational content contained within digitized microscopy specimens.
Collapse
Affiliation(s)
- Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA.
| | - Xin Qi
- Department of Pathology & Laboratory Medicine, Rutgers -- Robert Wood Johnson Medical School, New Brunswick, USA. .,Rutgers Cancer Institute of New Jersey, New Brunswick, USA.
| | - Daihou Wang
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, USA.
| | - Fusheng Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA. .,Department of Computer Science, Stony Brook University, Stony Brook, USA.
| | - George Teodoro
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA. .,Department of Computer Science, University of Brasilia, Brasília, Brazil.
| | - Lee Cooper
- Department of Biomedical Informatics, Emory University, Atlanta, USA.
| | - Michael Nalisnik
- Department of Biomedical Informatics, Emory University, Atlanta, USA.
| | - Lin Yang
- Department of Biomedical Engineering, University of Florida, Gainesville, USA.
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA.
| | - David J Foran
- Department of Pathology & Laboratory Medicine, Rutgers -- Robert Wood Johnson Medical School, New Brunswick, USA. .,Rutgers Cancer Institute of New Jersey, New Brunswick, USA.
| |
Collapse
|
47
|
Almeida JS, Hajagos J, Crnosija I, Kurc T, Saltz M, Saltz J. OpenHealth Platform for Interactive Contextualization of Population Health Open Data. AMIA Annu Symp Proc 2015; 2015:297-305. [PMID: 26958160 PMCID: PMC4765591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The financial incentives for data science applications leading to improved health outcomes, such as DSRIP (bit.ly/dsrip), are well-aligned with the broad adoption of Open Data by State and Federal agencies. This creates entirely novel opportunities for analytical applications that make exclusive use of the pervasive Web Computing platform. The framework described here explores this new avenue to contextualize Health data in a manner that relies exclusively on the native JavaScript interpreter and data processing resources of the ubiquitous Web Browser. The OpenHealth platform is made publicly available, and is publicly hosted with version control and open source, at https://github.com/mathbiol/openHealth. The different data/analytics workflow architectures explored are accompanied with live applications ranging from DSRIP, such as Hospital Inpatient Prevention Quality Indicators at http://bit.ly/pqiSuffolk, to The Cancer Genome Atlas (TCGA) as illustrated by http://bit.ly/tcgascopeGBM.
Collapse
Affiliation(s)
- Jonas S Almeida
- Dept Biomedical Informatics, Stony Brook University, State University of New York
| | - Janos Hajagos
- Dept Biomedical Informatics, Stony Brook University, State University of New York
| | - Ivan Crnosija
- Dept Biomedical Informatics, Stony Brook University, State University of New York
| | - Tahsin Kurc
- Dept Biomedical Informatics, Stony Brook University, State University of New York
| | - Mary Saltz
- Dept Radiology, Stony Brook University, State University of New York
| | - Joel Saltz
- Dept Biomedical Informatics, Stony Brook University, State University of New York
| |
Collapse
|
48
|
Gomes JM, Teodoro G, de Melo A, Kong J, Kurc T, Saltz JH. Efficient irregular wavefront propagation algorithms on Intel ® Xeon Phi ™. Proc Symp Comput Archit High Perform Comput 2015; 2015:25-32. [PMID: 27298591 DOI: 10.1109/sbac-pad.2015.13] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
We investigate the execution of the Irregular Wavefront Propagation Pattern (IWPP), a fundamental computing structure used in several image analysis operations, on the Intel® Xeon Phi™ co-processor. An efficient implementation of IWPP on the Xeon Phi is a challenging problem because of IWPP's irregularity and the use of atomic instructions in the original IWPP algorithm to resolve race conditions. On the Xeon Phi, the use of SIMD and vectorization instructions is critical to attain high performance. However, SIMD atomic instructions are not supported. Therefore, we propose a new IWPP algorithm that can take advantage of the supported SIMD instruction set. We also evaluate an alternate storage container (priority queue) to track active elements in the wavefront in an effort to improve the parallel algorithm efficiency. The new IWPP algorithm is evaluated with Morphological Reconstruction and Imfill operations as use cases. Our results show performance improvements of up to 5.63× on top of the original IWPP due to vectorization. Moreover, the new IWPP achieves speedups of 45.7× and 1.62×, respectively, as compared to efficient CPU and GPU implementations.
Collapse
Affiliation(s)
| | | | | | - Jun Kong
- Emory University, Atlanta, GA, USA
| | | | | |
Collapse
|
49
|
Teodoro G, Pan T, Kurc T, Kong J, Cooper L, Klasky S, Saltz J. Region Templates: Data Representation and Management for High-Throughput Image Analysis. Parallel Comput 2014; 40:589-610. [PMID: 26139953 PMCID: PMC4484879 DOI: 10.1016/j.parco.2014.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
We introduce a region template abstraction and framework for the efficient storage, management and processing of common data types in analysis of large datasets of high resolution images on clusters of hybrid computing nodes. The region template abstraction provides a generic container template for common data structures, such as points, arrays, regions, and object sets, within a spatial and temporal bounding box. It allows for different data management strategies and I/O implementations, while providing a homogeneous, unified interface to applications for data storage and retrieval. A region template application is represented as a hierarchical dataflow in which each computing stage may be represented as another dataflow of finer-grain tasks. The execution of the application is coordinated by a runtime system that implements optimizations for hybrid machines, including performance-aware scheduling for maximizing the utilization of computing devices and techniques to reduce the impact of data transfers between CPUs and GPUs. An experimental evaluation on a state-of-the-art hybrid cluster using a microscopy imaging application shows that the abstraction adds negligible overhead (about 3%) and achieves good scalability and high data transfer rates. Optimizations in a high speed disk based storage implementation of the abstraction to support asynchronous data transfers and computation result in an application performance gain of about 1.13×. Finally, a processing rate of 11,730 4K×4K tiles per minute was achieved for the microscopy imaging application on a cluster with 100 nodes (300 GPUs and 1,200 CPU cores). This computation rate enables studies with very large datasets.
Collapse
Affiliation(s)
- George Teodoro
- Department of Computer Science, University of Brasília, Brasília, DF, Brazil
| | - Tony Pan
- Biomedical Informatics Department, Emory University, Atlanta, GA, USA
| | - Tahsin Kurc
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jun Kong
- Biomedical Informatics Department, Emory University, Atlanta, GA, USA
| | - Lee Cooper
- Biomedical Informatics Department, Emory University, Atlanta, GA, USA
| | - Scott Klasky
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Joel Saltz
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
50
|
Abstract
XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups.
Collapse
Affiliation(s)
| | - Fusheng Wang
- Center for Comprehensive Informatics, Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA
| | - Tony Pan
- Center for Comprehensive Informatics, Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA
| | - Tahsin Kurc
- Center for Comprehensive Informatics, Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA
| | - Joel Saltz
- Center for Comprehensive Informatics, Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA
| |
Collapse
|