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Hanna MG, Olson NH, Zarella M, Dash RC, Herrmann MD, Furtado LV, Stram MN, Raciti PM, Hassell L, Mays A, Pantanowitz L, Sirintrapun JS, Krishnamurthy S, Parwani A, Lujan G, Evans A, Glassy EF, Bui MM, Singh R, Souers RJ, de Baca ME, Seheult JN. Recommendations for Performance Evaluation of Machine Learning in Pathology: A Concept Paper From the College of American Pathologists. Arch Pathol Lab Med 2023:497389. [PMID: 38041522 DOI: 10.5858/arpa.2023-0042-cp] [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] [Accepted: 09/11/2023] [Indexed: 12/03/2023]
Abstract
CONTEXT.— Machine learning applications in the pathology clinical domain are emerging rapidly. As decision support systems continue to mature, laboratories will increasingly need guidance to evaluate their performance in clinical practice. Currently there are no formal guidelines to assist pathology laboratories in verification and/or validation of such systems. These recommendations are being proposed for the evaluation of machine learning systems in the clinical practice of pathology. OBJECTIVE.— To propose recommendations for performance evaluation of in vitro diagnostic tests on patient samples that incorporate machine learning as part of the preanalytical, analytical, or postanalytical phases of the laboratory workflow. Topics described include considerations for machine learning model evaluation including risk assessment, predeployment requirements, data sourcing and curation, verification and validation, change control management, human-computer interaction, practitioner training, and competency evaluation. DATA SOURCES.— An expert panel performed a review of the literature, Clinical and Laboratory Standards Institute guidance, and laboratory and government regulatory frameworks. CONCLUSIONS.— Review of the literature and existing documents enabled the development of proposed recommendations. This white paper pertains to performance evaluation of machine learning systems intended to be implemented for clinical patient testing. Further studies with real-world clinical data are encouraged to support these proposed recommendations. Performance evaluation of machine learning models is critical to verification and/or validation of in vitro diagnostic tests using machine learning intended for clinical practice.
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Affiliation(s)
- Matthew G Hanna
- From the Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Hanna, Sirintrapun)
| | - Niels H Olson
- The Defense Innovation Unit, Mountain View, California (Olson)
- The Department of Pathology, Uniformed Services University, Bethesda, Maryland (Olson)
| | - Mark Zarella
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Zarella, Seheult)
| | - Rajesh C Dash
- Department of Pathology, Duke University Health System, Durham, North Carolina (Dash)
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston (Herrmann)
| | - Larissa V Furtado
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee (Furtado)
| | - Michelle N Stram
- The Department of Forensic Medicine, New York University, and Office of Chief Medical Examiner, New York (Stram)
| | | | - Lewis Hassell
- Department of Pathology, Oklahoma University Health Sciences Center, Oklahoma City (Hassell)
| | - Alex Mays
- The MITRE Corporation, McLean, Virginia (Mays)
| | - Liron Pantanowitz
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor (Pantanowitz)
| | - Joseph S Sirintrapun
- From the Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Hanna, Sirintrapun)
| | | | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus (Parwani, Lujan)
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus (Parwani, Lujan)
| | - Andrew Evans
- Laboratory Medicine, Mackenzie Health, Toronto, Ontario, Canada (Evans)
| | - Eric F Glassy
- Affiliated Pathologists Medical Group, Rancho Dominguez, California (Glassy)
| | - Marilyn M Bui
- Departments of Pathology and Machine Learning, Moffitt Cancer Center, Tampa, Florida (Bui)
| | - Rajendra Singh
- Department of Dermatopathology, Summit Health, Summit Woodland Park, New Jersey (Singh)
| | - Rhona J Souers
- Department of Biostatistics, College of American Pathologists, Northfield, Illinois (Souers)
| | | | - Jansen N Seheult
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Zarella, Seheult)
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Schacherer DP, Herrmann MD, Clunie DA, Höfener H, Clifford W, Longabaugh WJR, Pieper S, Kikinis R, Fedorov A, Homeyer A. The NCI Imaging Data Commons as a platform for reproducible research in computational pathology. Comput Methods Programs Biomed 2023; 242:107839. [PMID: 37832430 PMCID: PMC10841477 DOI: 10.1016/j.cmpb.2023.107839] [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] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 09/20/2023] [Accepted: 10/01/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. METHODS Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. RESULTS The results of different runs of the same experiment were reproducible to a large extent. However, we observed occasional, small variations in AUC values, indicating a practical limit to reproducibility. CONCLUSIONS We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.
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Affiliation(s)
- Daniela P Schacherer
- Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Henning Höfener
- Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany
| | | | | | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - André Homeyer
- Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany.
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Fedorov A, Longabaugh WJR, Pot D, Clunie DA, Pieper SD, Gibbs DL, Bridge C, Herrmann MD, Homeyer A, Lewis R, Aerts HJWL, Krishnaswamy D, Thiriveedhi VK, Ciausu C, Schacherer DP, Bontempi D, Pihl T, Wagner U, Farahani K, Kim E, Kikinis R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. Radiographics 2023; 43:e230180. [PMID: 37999984 DOI: 10.1148/rg.230180] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2023]
Abstract
The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires easy access to large high-quality annotated datasets, which are both representative and diverse. The National Cancer Institute (NCI) Imaging Data Commons (IDC) hosts large and diverse publicly available cancer image data collections. By harmonizing all data based on industry standards and colocalizing it with analysis and exploration resources, the IDC aims to facilitate the development, validation, and clinical translation of AI tools and address the well-documented challenges of establishing reproducible and transparent AI processing pipelines. Balanced use of established commercial products with open-source solutions, interconnected by standard interfaces, provides value and performance, while preserving sufficient agility to address the evolving needs of the research community. Emphasis on the development of tools, use cases to demonstrate the utility of uniform data representation, and cloud-based analysis aim to ease adoption and help define best practices. Integration with other data in the broader NCI Cancer Research Data Commons infrastructure opens opportunities for multiomics studies incorporating imaging data to further empower the research community to accelerate breakthroughs in cancer detection, diagnosis, and treatment. Published under a CC BY 4.0 license.
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Affiliation(s)
- Andrey Fedorov
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - William J R Longabaugh
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - David Pot
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - David A Clunie
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Steven D Pieper
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - David L Gibbs
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Christopher Bridge
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Markus D Herrmann
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - André Homeyer
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Rob Lewis
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Hugo J W L Aerts
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Deepa Krishnaswamy
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Vamsi Krishna Thiriveedhi
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Cosmin Ciausu
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Daniela P Schacherer
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Dennis Bontempi
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Todd Pihl
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Ulrike Wagner
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Keyvan Farahani
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Erika Kim
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
| | - Ron Kikinis
- From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.)
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4
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Gorman C, Punzo D, Octaviano I, Pieper S, Longabaugh WJR, Clunie DA, Kikinis R, Fedorov AY, Herrmann MD. Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology. Nat Commun 2023; 14:1572. [PMID: 36949078 PMCID: PMC10033920 DOI: 10.1038/s41467-023-37224-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.
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Affiliation(s)
- Chris Gorman
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrey Y Fedorov
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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5
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Papazoglou AS, Karagiannidis E, Liatsos A, Bompoti A, Moysidis DV, Arvanitidis C, Tsolaki F, Tsagkaropoulos S, Theocharis S, Tagarakis G, Michaelson JS, Herrmann MD. Volumetric Tissue Imaging of Surgical Tissue Specimens Using Micro-Computed Tomography: An Emerging Digital Pathology Modality for Nondestructive, Slide-Free Microscopy-Clinical Applications of Digital Pathology in 3 Dimensions. Am J Clin Pathol 2023; 159:242-254. [PMID: 36478204 DOI: 10.1093/ajcp/aqac143] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/14/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES Micro-computed tomography (micro-CT) is a novel, nondestructive, slide-free digital imaging modality that enables the acquisition of high-resolution, volumetric images of intact surgical tissue specimens. The aim of this systematic mapping review is to provide a comprehensive overview of the available literature on clinical applications of micro-CT tissue imaging and to assess its relevance and readiness for pathology practice. METHODS A computerized literature search was performed in the PubMed, Scopus, Web of Science, and CENTRAL databases. To gain insight into regulatory and financial considerations for performing and examining micro-CT imaging procedures in a clinical setting, additional searches were performed in medical device databases. RESULTS Our search identified 141 scientific articles published between 2000 and 2021 that described clinical applications of micro-CT tissue imaging. The number of relevant publications is progressively increasing, with the specialties of pulmonology, cardiology, otolaryngology, and oncology being most commonly concerned. The included studies were mostly performed in pathology departments. Current micro-CT devices have already been cleared for clinical use, and a Current Procedural Terminology (CPT) code exists for reimbursement of micro-CT imaging procedures. CONCLUSIONS Micro-CT tissue imaging enables accurate volumetric measurements and evaluations of entire surgical specimens at microscopic resolution across a wide range of clinical applications.
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Affiliation(s)
| | - Efstratios Karagiannidis
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Alexandros Liatsos
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreana Bompoti
- Diagnostic Imaging, Peterborough City Hospital, North West Anglia NHS Foundation Trust, Peterborough, UK
| | - Dimitrios V Moysidis
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Christos Arvanitidis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece.,LifeWatch ERIC, Sector II-II, Seville, Spain
| | - Fani Tsolaki
- Department of Cardiothoracic Surgery, AHEPA University Hospital, Thessaloniki, Greece
| | | | - Stamatios Theocharis
- First Department of Pathology, National and Kapoditrian University of Athens, Athens, Greece
| | - Georgios Tagarakis
- Department of Cardiothoracic Surgery, AHEPA University Hospital, Thessaloniki, Greece
| | - James S Michaelson
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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6
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Berbís MA, McClintock DS, Bychkov A, Van der Laak J, Pantanowitz L, Lennerz JK, Cheng JY, Delahunt B, Egevad L, Eloy C, Farris AB, Fraggetta F, García del Moral R, Hartman DJ, Herrmann MD, Hollemans E, Iczkowski KA, Karsan A, Kriegsmann M, Salama ME, Sinard JH, Tuthill JM, Williams B, Casado-Sánchez C, Sánchez-Turrión V, Luna A, Aneiros-Fernández J, Shen J. Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade. EBioMedicine 2023; 88:104427. [PMID: 36603288 PMCID: PMC9823157 DOI: 10.1016/j.ebiom.2022.104427] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [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: 10/13/2022] [Revised: 12/06/2022] [Accepted: 12/13/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation. FUNDING No specific funding was provided for this study.
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Affiliation(s)
- M. Alvaro Berbís
- Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain,Faculty of Medicine, Autonomous University of Madrid, Madrid, Spain,Corresponding author. Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, 14011, Spain.
| | - David S. McClintock
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan
| | - Jeroen Van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Jochen K. Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Jerome Y. Cheng
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Brett Delahunt
- Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand
| | - Lars Egevad
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Catarina Eloy
- Pathology Laboratory, Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
| | - Alton B. Farris
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Filippo Fraggetta
- Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy
| | | | - Douglas J. Hartman
- Department of Anatomic Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Markus D. Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Eva Hollemans
- Department of Pathology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Aly Karsan
- Department of Pathology & Laboratory Medicine, University of British Columbia, Michael Smith Genome Sciences Centre, Vancouver, Canada
| | - Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | | | - John H. Sinard
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - J. Mark Tuthill
- Department of Pathology, Henry Ford Hospital, Detroit, MI, USA
| | - Bethany Williams
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - César Casado-Sánchez
- Department of Plastic and Reconstructive Surgery, La Paz University Hospital, Madrid, Spain
| | - Víctor Sánchez-Turrión
- Department of General Surgery and Digestive Tract, Puerta de Hierro-Majadahonda University Hospital, Madrid, Spain
| | - Antonio Luna
- Department of Integrated Diagnostics, HT Médica, Clínica Las Nieves, Jaén, Spain
| | - José Aneiros-Fernández
- Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain,Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy
| | - Jeanne Shen
- Department of Pathology and Center for Artificial Intelligence in Medicine & Imaging, Stanford University School of Medicine, Stanford, CA, USA.
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7
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Schapiro D, Yapp C, Sokolov A, Reynolds SM, Chen YA, Sudar D, Xie Y, Muhlich J, Arias-Camison R, Arena S, Taylor AJ, Nikolov M, Tyler M, Lin JR, Burlingame EA, Chang YH, Farhi SL, Thorsson V, Venkatamohan N, Drewes JL, Pe'er D, Gutman DA, Herrmann MD, Gehlenborg N, Bankhead P, Roland JT, Herndon JM, Snyder MP, Angelo M, Nolan G, Swedlow JR, Schultz N, Merrick DT, Mazzili SA, Cerami E, Rodig SJ, Santagata S, Sorger PK. MITI minimum information guidelines for highly multiplexed tissue images. Nat Methods 2022; 19:262-267. [PMID: 35277708 PMCID: PMC9009186 DOI: 10.1038/s41592-022-01415-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.
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Affiliation(s)
- Denis Schapiro
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Image and Data Analysis Core, Harvard Medical School, Boston, MA, USA
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Yu-An Chen
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, OR, USA
| | - Yubin Xie
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jeremy Muhlich
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Raquel Arias-Camison
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Sarah Arena
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | | | | | - Madison Tyler
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Erik A Burlingame
- Oregon Health and Science University, Portland, OR, USA
- Indica Labs, Albuquerque, NM, USA
| | - Young H Chang
- Oregon Health and Science University, Portland, OR, USA
| | - Samouil L Farhi
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Julia L Drewes
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dana Pe'er
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Peter Bankhead
- Edinburgh Pathology, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Joseph T Roland
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - John M Herndon
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Michael Angelo
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Garry Nolan
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Jason R Swedlow
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | - Nikolaus Schultz
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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8
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Dudgeon SN, Wen S, Hanna MG, Gupta R, Amgad M, Sheth M, Marble H, Huang R, Herrmann MD, Szu CH, Tong D, Werness B, Szu E, Larsimont D, Madabhushi A, Hytopoulos E, Chen W, Singh R, Hart SN, Sharma A, Saltz J, Salgado R, Gallas BD. A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study. J Pathol Inform 2021; 12:45. [PMID: 34881099 PMCID: PMC8609287 DOI: 10.4103/jpi.jpi_83_20] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/23/2021] [Accepted: 03/16/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose: Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images. We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer. Methods: We digitized 64 glass slides of hematoxylin- and eosin-stained invasive ductal carcinoma core biopsies prepared at a single clinical site. A collaborating pathologist selected 10 regions of interest (ROIs) per slide for evaluation. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The microscope platform allows the same ROIs to be evaluated in both modes. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI. Results: In total, 19 pathologists made 1645 ROI evaluations during a data collection event and the following 2 weeks. The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm. Conclusion: We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will investigate methods to use the dataset as an external validation tool for algorithms. We will also consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the Food and Drug Administration via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned.
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Affiliation(s)
- Sarah N Dudgeon
- Division of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
| | - Si Wen
- Division of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
| | | | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Mohamed Amgad
- Department of Pathology, Northwestern University, Chicago, IL, USA
| | - Manasi Sheth
- Division of Biostatistics, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
| | - Hetal Marble
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Richard Huang
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | - Evan Szu
- Arrive Bio, San Francisco, CA, USA
| | - Denis Larsimont
- Department of Pathology, Institute Jules Bordet, Brussels, Belgium
| | - Anant Madabhushi
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA
| | | | - Weijie Chen
- Division of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
| | - Rajendra Singh
- Northwell Health and Zucker School of Medicine, New York, NY, USA
| | - Steven N Hart
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Roberto Salgado
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia.,Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
| | - Brandon D Gallas
- Division of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
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9
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Bompoti A, Papazoglou AS, Moysidis DV, Otountzidis N, Karagiannidis E, Stalikas N, Panteris E, Ganesh V, Sanctuary T, Arvanitidis C, Sianos G, Michaelson JS, Herrmann MD. Volumetric Imaging of Lung Tissue at Micrometer Resolution: Clinical Applications of Micro-CT for the Diagnosis of Pulmonary Diseases. Diagnostics (Basel) 2021; 11:diagnostics11112075. [PMID: 34829422 PMCID: PMC8625264 DOI: 10.3390/diagnostics11112075] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 11/16/2022] Open
Abstract
Micro-computed tomography (micro-CT) is a promising novel medical imaging modality that allows for non-destructive volumetric imaging of surgical tissue specimens at high spatial resolution. The aim of this study is to provide a comprehensive assessment of the clinical applications of micro-CT for the tissue-based diagnosis of lung diseases. This scoping review was conducted in accordance with the PRISMA Extension for Scoping Reviews, aiming to include every clinical study reporting on micro-CT imaging of human lung tissues. A literature search yielded 570 candidate articles, out of which 37 were finally included in the review. Of the selected studies, 9 studies explored via micro-CT imaging the morphology and anatomy of normal human lung tissue; 21 studies investigated microanatomic pulmonary alterations due to obstructive or restrictive lung diseases, such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and cystic fibrosis; and 7 studies examined the utility of micro-CT imaging in assessing lung cancer lesions (n = 4) or in transplantation-related pulmonary alterations (n = 3). The selected studies reported that micro-CT could successfully detect several lung diseases providing three-dimensional images of greater detail and resolution than routine optical slide microscopy, and could additionally provide valuable volumetric insight in both restrictive and obstructive lung diseases. In conclusion, micro-CT-based volumetric measurements and qualitative evaluations of pulmonary tissue structures can be utilized for the clinical management of a variety of lung diseases. With micro-CT devices becoming more accessible, the technology has the potential to establish itself as a core diagnostic imaging modality in pathology and to enable integrated histopathologic and radiologic assessment of lung cancer and other lung diseases.
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Affiliation(s)
- Andreana Bompoti
- Department of Radiology, Peterborough City Hospital, Northwest Anglia NHS Foundation Trust, Peterborough PE3 9GZ, UK;
| | - Andreas S. Papazoglou
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636 Thessaloniki, Greece; (A.S.P.); (D.V.M.); (N.O.); (E.K.); (N.S.); (G.S.)
| | - Dimitrios V. Moysidis
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636 Thessaloniki, Greece; (A.S.P.); (D.V.M.); (N.O.); (E.K.); (N.S.); (G.S.)
| | - Nikolaos Otountzidis
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636 Thessaloniki, Greece; (A.S.P.); (D.V.M.); (N.O.); (E.K.); (N.S.); (G.S.)
| | - Efstratios Karagiannidis
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636 Thessaloniki, Greece; (A.S.P.); (D.V.M.); (N.O.); (E.K.); (N.S.); (G.S.)
| | - Nikolaos Stalikas
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636 Thessaloniki, Greece; (A.S.P.); (D.V.M.); (N.O.); (E.K.); (N.S.); (G.S.)
| | - Eleftherios Panteris
- Biomic_AUTh, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center B1.4, 10th km Thessaloniki-Thermi Rd., P.O. Box 8318, GR 57001 Thessaloniki, Greece;
| | | | - Thomas Sanctuary
- Respiratory Department, Medway NHS Foundation Trust, Kent ME7 5NY, UK;
| | - Christos Arvanitidis
- Hellenic Centre for Marine Research (HCMR), Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), 70013 Heraklion, Greece;
- LifeWatch ERIC, Sector II-II, Plaza de España, 41071 Seville, Spain
| | - Georgios Sianos
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636 Thessaloniki, Greece; (A.S.P.); (D.V.M.); (N.O.); (E.K.); (N.S.); (G.S.)
| | - James S. Michaelson
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Markus D. Herrmann
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA;
- Correspondence: ; Tel.: +6-17-724-1896
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10
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Fedorov A, Beichel R, Kalpathy-Cramer J, Clunie D, Onken M, Riesmeier J, Herz C, Bauer C, Beers A, Fillion-Robin JC, Lasso A, Pinter C, Pieper S, Nolden M, Maier-Hein K, Herrmann MD, Saltz J, Prior F, Fennessy F, Buatti J, Kikinis R. Quantitative Imaging Informatics for Cancer Research. JCO Clin Cancer Inform 2021; 4:444-453. [PMID: 32392097 PMCID: PMC7265794 DOI: 10.1200/cci.19.00165] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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: 01/06/2023] Open
Abstract
PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.
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Affiliation(s)
- Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | | | | | | | | | - Christian Herz
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | | | | | | | | | | | - Marco Nolden
- German Cancer Research Center, Heidelberg, Germany
| | | | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | | | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR
| | - Fiona Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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11
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Fedorov A, Longabaugh WJR, Pot D, Clunie DA, Pieper S, Aerts HJWL, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, Clifford W, Herrmann MD, Höfener H, Octaviano I, Osborne C, Paquette S, Petts J, Punzo D, Reyes M, Schacherer DP, Tian M, White G, Ziegler E, Shmulevich I, Pihl T, Wagner U, Farahani K, Kikinis R. NCI Imaging Data Commons. Cancer Res 2021; 81:4188-4193. [PMID: 34185678 PMCID: PMC8373794 DOI: 10.1158/0008-5472.can-21-0950] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/25/2021] [Accepted: 06/14/2021] [Indexed: 11/16/2022]
Abstract
The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data. We achieve this goal by colocating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research. SIGNIFICANCE: This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.
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Affiliation(s)
- Andrey Fedorov
- Brigham and Women's Hospital, Department of Radiology, Harvard Medical School, Boston, Massachusetts.
| | | | - David Pot
- General Dynamics, Bethesda, Maryland
| | | | | | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.,Departments of Radiation Oncology & Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.,Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
| | | | - Rob Lewis
- Radical Imaging, Boston, Massachusetts
| | - Afshin Akbarzadeh
- Brigham and Women's Hospital, Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Dennis Bontempi
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
| | | | - Markus D Herrmann
- Massachusetts General Hospital, Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | | | | | | | | | | | | | | | | | - Mi Tian
- Institute for Systems Biology, Seattle, Washington
| | - George White
- Institute for Systems Biology, Seattle, Washington
| | | | | | - Todd Pihl
- Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Ulrike Wagner
- Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | | | - Ron Kikinis
- Brigham and Women's Hospital, Department of Radiology, Harvard Medical School, Boston, Massachusetts
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12
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Papazoglou AS, Karagiannidis E, Moysidis DV, Sofidis G, Bompoti A, Stalikas N, Panteris E, Arvanitidis C, Herrmann MD, Michaelson JS, Sianos G. Current clinical applications and potential perspective of micro-computed tomography in cardiovascular imaging: A systematic scoping review. Hellenic J Cardiol 2021; 62:399-407. [PMID: 33991670 DOI: 10.1016/j.hjc.2021.04.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 02/01/2021] [Revised: 03/20/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Micro-computed tomography (micro-CT) constitutes an emerging imaging technique, which can be utilized in cardiovascular medicine to study in-detail the microstructure of heart and vessels. This paper aims to systematically review the clinical utility of micro-CT in cardiovascular imaging and propose future applications of micro-CT imaging in cardiovascular research. A systematic scoping review was conducted by searching for original studies written in English according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews. Medline, Scopus, ClinicalTrials.gov, and the Cochrane library were systematically searched through December 11, 2020 to identify publications concerning micro-CT applications in cardiovascular imaging. Preclinical-animal studies and case reports were excluded. The Newcastle-Ottawa assessment scale for observational studies was used to evaluate study quality. In total, the search strategy identified 30 studies that report on micro-CT-based cardiovascular imaging and satisfy our eligibility criteria. Across all included studies, the total number of micro-CT scanned specimens was 1,227. Six studies involved postmortem 3D-reconstruction of congenital heart defects, while eleven studies described atherosclerotic vessel (coronary or carotid) characteristics. Thirteen other studies employed micro-CT for the assessment of medical devices (mainly stents or prosthetic valves). In conclusion, micro-CT is a novel imaging modality, effectively adapted for the 3D visualization and analysis of cardiac soft tissues and devices at high spatial resolution. Its increasing use could make significant contributions to our improved understanding of the histopathophysiology of cardiovascular diseases, and, thus, has the potential to optimize interventional procedures and technologies, and ultimately improve patient outcomes.
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Affiliation(s)
- Andreas S Papazoglou
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636, Thessaloniki, Greece
| | - Efstratios Karagiannidis
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636, Thessaloniki, Greece
| | - Dimitrios V Moysidis
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636, Thessaloniki, Greece
| | - Georgios Sofidis
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636, Thessaloniki, Greece
| | | | - Nikolaos Stalikas
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636, Thessaloniki, Greece
| | - Eleftherios Panteris
- Biomic_AUTh, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, B1.4, Thessaloniki, 10th km Thessaloniki-Thermi Rd, P.O. Box 8318, GR 57001, Greece
| | - Christos Arvanitidis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Crete, 70013, Greece; LifeWatch ERIC, Sector II-II, Plaza de España, 41071, Seville, Spain
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - James S Michaelson
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Georgios Sianos
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54636, Thessaloniki, Greece.
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13
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Dash RC, Jones N, Merrick R, Haroske G, Harrison J, Sayers C, Haarselhorst N, Wintell M, Herrmann MD, Macary F. Integrating the Health-care Enterprise Pathology and Laboratory Medicine Guideline for Digital Pathology Interoperability. J Pathol Inform 2021; 12:16. [PMID: 34221632 PMCID: PMC8240547 DOI: 10.4103/jpi.jpi_98_20] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 10/23/2020] [Revised: 12/03/2020] [Accepted: 01/11/2021] [Indexed: 11/17/2022] Open
Abstract
Integrating the health-care enterprise (IHE) is an international initiative to promote the use of standards to achieve interoperability among health information technology systems. The Pathology and Laboratory Medicine domain within IHE has brought together subject matter experts, electronic health record vendors, and digital imaging vendors, to initiate development of a series of digital pathology interoperability guidelines, called “integration profiles” within IHE. This effort begins with documentation of common use cases, followed by identification of available data and technology standards best utilized to achieve those use cases. An integration profile that describes the information flow and technology interactions is then published for trial use. Real world testing occurs in “connectathon” events, in which multiple vendors attempt to connect their products following the interoperability guidance parameters set forth in the profile. This paper describes the overarching set of integration profiles, one of which has been published, to support key digital pathology use cases.
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Affiliation(s)
- Rajesh C Dash
- Department of Pathology, Duke University Health System, Durham, NC, USA
| | - Nicholas Jones
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Gunter Haroske
- Bundesverband Deutscher Pathologen e.V., Robert-Koch-Platz 9 D-10115, Berlin, Germany
| | - James Harrison
- Department of Pathology, University of Virginia, Hospital West Complex, Charlottesville, VA, USA
| | - Craig Sayers
- Department of Histopathology, Mid Yorkshire Hospitals NHS Trust, National Pathology Imaging Co-operative, Dewsbury and District Hospital, Dewsbury, WF13 4HS, United Kingdom
| | - Nick Haarselhorst
- PHILIPS Digital and Computational Pathology, Veenpluis 4-6, Building QY-1.077D 5684PC, Best, Netherlands
| | - Mikael Wintell
- Västra Gotalandsregionen/DICOM, Vastra Gotalandsregionen FVM, Flojelbergsgatan 2A, 431 45 Molndal Sweden
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - François Macary
- Semantic Interoperability Services, PHAST Services, 25 rue du Louvre, 75001, Paris France
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14
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Herrmann MD, Lennerz JK. [Technical, operational, and regulatory considerations for the adoption of digital and computational pathology]. Pathologe 2021; 41:103-110. [PMID: 33263808 DOI: 10.1007/s00292-020-00871-z] [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] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
BACKGROUND Innovative information technologies open new possibilities for diagnostics and promise to improve patient care. However, the integration of data- and computing-intensive procedures into diagnostic workflows also poses risks and considerable challenges for pathologists. OBJECTIVES Considering technical, operational, and regulatory aspects, we present a holistic and systematic approach for the adoption of digital and computational pathology. MATERIAL AND METHODS We discuss challenges for the implementation of computational diagnostic procedures and analyze regulatory frameworks for risk-based assessment and monitoring of software as an in vitro diagnostic device. Applying regulatory science, we develop an approach to streamline adoption of digital workflows in pathology. RESULTS Data- and computing-intensive workflows in digital pathology are complex and underscore the need for computational and regulatory science as a central part of pathological diagnostics. To promote the adoption of computational diagnostics, we have founded an interdisciplinary initiative (the Alliance) that focuses on regulatory research in the field of digital pathology and works closely with a number of expert and interest groups on the precompetitive development of standards for computational workflows. DISCUSSION The inclusion of different stakeholder groups and the coordination of technical, operational, and regulatory aspects is necessary to maintain the balance between progress and safety in diagnostics and to make innovations quickly and safely available for patient care.
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Affiliation(s)
- Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. .,Pathology Service, Massachusetts General Hospital, 55 Fruit Street, 02114, Boston, MA, USA.
| | - Jochen K Lennerz
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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15
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Marble HD, Huang R, Dudgeon SN, Lowe A, Herrmann MD, Blakely S, Leavitt MO, Isaacs M, Hanna MG, Sharma A, Veetil J, Goldberg P, Schmid JH, Lasiter L, Gallas BD, Abels E, Lennerz JK. A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients. J Pathol Inform 2020; 11:22. [PMID: 33042601 PMCID: PMC7518200 DOI: 10.4103/jpi.jpi_27_20] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [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: 04/01/2020] [Revised: 04/20/2020] [Accepted: 06/16/2020] [Indexed: 12/13/2022] Open
Abstract
Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology (the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.
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Affiliation(s)
- Hetal Desai Marble
- Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Richard Huang
- Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Sarah Nixon Dudgeon
- Division of Imaging, Diagnostics, and Software Reliability, Center for Devices and Radiological Health, Food and Drug Administration, Office of Science and Engineering Laboratories, Silver Spring, MD, USA
| | | | - Markus D Herrmann
- Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Mike Isaacs
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Matthew G Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Jithesh Veetil
- Medical Device Innovation Consortium, Arlington, VA, USA
| | | | | | | | - Brandon D Gallas
- Division of Imaging, Diagnostics, and Software Reliability, Center for Devices and Radiological Health, Food and Drug Administration, Office of Science and Engineering Laboratories, Silver Spring, MD, USA
| | | | - Jochen K Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
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16
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Herrmann MD, Clunie DA, Fedorov A, Doyle SW, Pieper S, Klepeis V, Le LP, Mutter GL, Milstone DS, Schultz TJ, Kikinis R, Kotecha GK, Hwang DH, Andriole KP, Iafrate AJ, Brink JA, Boland GW, Dreyer KJ, Michalski M, Golden JA, Louis DN, Lennerz JK. Implementing the DICOM Standard for Digital Pathology. J Pathol Inform 2018; 9:37. [PMID: 30533276 PMCID: PMC6236926 DOI: 10.4103/jpi.jpi_42_18] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [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: 07/30/2018] [Accepted: 08/06/2018] [Indexed: 11/29/2022] Open
Abstract
Background: Digital Imaging and Communications in Medicine (DICOM®) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. Methods: We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. We generated the files using image data from four vendor-specific image file formats and clinical metadata from two departments with different laboratory information systems. We validated the generated DICOM files using recognized DICOM validation tools and measured encoding, storage, and access efficiency for three image compression methods. Finally, we evaluated storing, querying, and retrieving data over the web using existing DICOM archive software. Results: Whole slide image data can be encoded together with relevant patient and specimen-related metadata as DICOM objects. These objects can be accessed efficiently from files or through RESTful web services using existing software implementations. Performance measurements show that the choice of image compression method has a major impact on data access efficiency. For lossy compression, JPEG achieves the fastest compression/decompression rates. For lossless compression, JPEG-LS significantly outperforms JPEG 2000 with respect to data encoding and decoding speed. Conclusion: Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology.
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Affiliation(s)
| | | | - Andriy Fedorov
- Department of Radiology, Surgical Planning Laboratory, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Sean W Doyle
- MGH and BWH Center for Clinical Data Science, Boston, MA, USA
| | | | - Veronica Klepeis
- Harvard Medical School, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Long P Le
- Harvard Medical School, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - George L Mutter
- Harvard Medical School, Boston, MA, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - David S Milstone
- Harvard Medical School, Boston, MA, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Thomas J Schultz
- Enterprise Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Ron Kikinis
- Department of Radiology, Surgical Planning Laboratory, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Gopal K Kotecha
- MGH and BWH Center for Clinical Data Science, Boston, MA, USA
| | - David H Hwang
- Harvard Medical School, Boston, MA, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Katherine P Andriole
- MGH and BWH Center for Clinical Data Science, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - A John Iafrate
- Harvard Medical School, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - James A Brink
- Harvard Medical School, Boston, MA, USA.,Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Giles W Boland
- Harvard Medical School, Boston, MA, USA.,Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Keith J Dreyer
- MGH and BWH Center for Clinical Data Science, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Mark Michalski
- MGH and BWH Center for Clinical Data Science, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jeffrey A Golden
- Harvard Medical School, Boston, MA, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - David N Louis
- Harvard Medical School, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Jochen K Lennerz
- Harvard Medical School, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
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17
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Gut G, Herrmann MD, Pelkmans L. Multiplexed protein maps link subcellular organization to cellular states. Science 2018; 361:361/6401/eaar7042. [PMID: 30072512 DOI: 10.1126/science.aar7042] [Citation(s) in RCA: 244] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 03/23/2018] [Accepted: 06/21/2018] [Indexed: 12/18/2022]
Abstract
Obtaining highly multiplexed protein measurements across multiple length scales has enormous potential for biomedicine. Here, we measured, by iterative indirect immunofluorescence imaging (4i), 40-plex protein readouts from biological samples at high-throughput from the millimeter to the nanometer scale. This approach simultaneously captures properties apparent at the population, cellular, and subcellular levels, including microenvironment, cell shape, and cell cycle state. It also captures the detailed morphology of organelles, cytoskeletal structures, nuclear subcompartments, and the fate of signaling receptors in thousands of single cells in situ. We used computer vision and systems biology approaches to achieve unsupervised comprehensive quantification of protein subcompartmentalization within various multicellular, cellular, and pharmacological contexts. Thus, highly multiplexed subcellular protein maps can be used to identify functionally relevant single-cell states.
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Affiliation(s)
- Gabriele Gut
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland. .,Molecular Life Sciences PhD Program, Life Science Zurich Graduate School, University of Zurich, Zurich, Switzerland
| | - Markus D Herrmann
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.,MD-PhD and Systems Biology PhD Program, Life Science Zurich Graduate School, University of Zurich, Zurich, Switzerland
| | - Lucas Pelkmans
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
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18
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Stoeger T, Battich N, Herrmann MD, Yakimovich Y, Pelkmans L. Computer vision for image-based transcriptomics. Methods 2015; 85:44-53. [DOI: 10.1016/j.ymeth.2015.05.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 04/13/2015] [Accepted: 05/17/2015] [Indexed: 12/14/2022] Open
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Nagel PD, Stenzinger A, Feld FM, Herrmann MD, Brüderlein S, Barth TFE, Marienfeld R, Endris V, Weichert W, Debatin KM, Westhoff MA, Lessel D, Möller P, Lennerz JK. KIT mutations in primary mediastinal B-cell lymphoma. Blood Cancer J 2014; 4:e241. [PMID: 25148223 PMCID: PMC4219474 DOI: 10.1038/bcj.2014.61] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [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: 01/13/2023] Open
Affiliation(s)
- P D Nagel
- Institute of Pathology, University Ulm, Ulm, Germany
| | - A Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - F M Feld
- Institute of Pathology, University Ulm, Ulm, Germany
| | - M D Herrmann
- 1] Institute of Pathology, University Ulm, Ulm, Germany [2] Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - S Brüderlein
- Institute of Pathology, University Ulm, Ulm, Germany
| | - T F E Barth
- Institute of Pathology, University Ulm, Ulm, Germany
| | - R Marienfeld
- Institute of Pathology, University Ulm, Ulm, Germany
| | - V Endris
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - W Weichert
- 1] Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany [2] National Center of Tumor Diseases (NCT), Heidelberg, Germany
| | - K-M Debatin
- Department of Pediatrics and Adolescent Medicine, University Medical Center, Ulm, Germany
| | - M-A Westhoff
- Department of Pediatrics and Adolescent Medicine, University Medical Center, Ulm, Germany
| | - D Lessel
- 1] Institute of Human Genetics, University Ulm, Ulm, Germany [2] Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - P Möller
- Institute of Pathology, University Ulm, Ulm, Germany
| | - J K Lennerz
- Institute of Pathology, University Ulm, Ulm, Germany
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Herrmann MD, Lennerz JK, Bullinger L, Bartholomae S, Holzmann K, Westhoff MA, Corbacioglu S, Debatin KM. Transitory dasatinib-resistant states in KITmut t(8;21) acute myeloid leukemia cells correlate with altered KIT expression. Exp Hematol 2014; 42:90-100. [DOI: 10.1016/j.exphem.2013.10.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2013] [Revised: 09/30/2013] [Accepted: 10/23/2013] [Indexed: 11/29/2022]
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