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Trares K, Wiesenfarth M, Stocker H, Perna L, Petrera A, Hauck SM, Beyreuther K, Brenner H, Schöttker B. Addition of inflammation-related biomarkers to the CAIDE model for risk prediction of all-cause dementia, Alzheimer's disease and vascular dementia in a prospective study. Immun Ageing 2024; 21:23. [PMID: 38570813 PMCID: PMC10988812 DOI: 10.1186/s12979-024-00427-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND It is of interest whether inflammatory biomarkers can improve dementia prediction models, such as the widely used Cardiovascular Risk Factors, Aging and Dementia (CAIDE) model. METHODS The Olink Target 96 Inflammation panel was assessed in a nested case-cohort design within a large, population-based German cohort study (n = 9940; age-range: 50-75 years). All study participants who developed dementia over 20 years of follow-up and had complete CAIDE variable data (n = 562, including 173 Alzheimer's disease (AD) and 199 vascular dementia (VD) cases) as well as n = 1,356 controls were selected for measurements. 69 inflammation-related biomarkers were eligible for use. LASSO logistic regression and bootstrapping were utilized to select relevant biomarkers and determine areas under the curve (AUCs). RESULTS The CAIDE model 2 (including Apolipoprotein E (APOE) ε4 carrier status) predicted all-cause dementia, AD, and VD better than CAIDE model 1 (without APOE ε4) with AUCs of 0.725, 0.752 and 0.707, respectively. Although 20, 7, and 4 inflammation-related biomarkers were selected by LASSO regression to improve CAIDE model 2, the AUCs did not increase markedly. CAIDE models 1 and 2 generally performed better in mid-life (50-64 years) than in late-life (65-75 years) sub-samples of our cohort, but again, inflammation-related biomarkers did not improve their predictive abilities. CONCLUSIONS Despite a lack of improvement in dementia risk prediction, the selected inflammation-related biomarkers were significantly associated with dementia outcomes and may serve as a starting point to further elucidate the pathogenesis of dementia.
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Affiliation(s)
- Kira Trares
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, 69120, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Hannah Stocker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, 69120, Germany
| | - Laura Perna
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, Munich, 80804, Germany
- Division of Mental Health of Older Adults, Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Agnese Petrera
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Stefanie M Hauck
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Konrad Beyreuther
- Network Aging Research, Heidelberg University, Bergheimer Straße 20, Heidelberg, 69115, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, 69120, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, 69120, Germany.
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2
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Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, Glocker B, Isensee F, Kleesiek J, Kozubek M, Reyes M, Riegler MA, Wiesenfarth M, Kavur AE, Sudre CH, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Rädsch T, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Blaschko MB, Cardoso MJ, Cheplygina V, Cimini BA, Collins GS, Farahani K, Ferrer L, Galdran A, van Ginneken B, Haase R, Hashimoto DA, Hoffman MM, Huisman M, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Karthikesalingam A, Kofler F, Kopp-Schneider A, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Mattson P, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rajpoot N, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, van Smeden M, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Jäger PF. Metrics reloaded: recommendations for image analysis validation. Nat Methods 2024; 21:195-212. [PMID: 38347141 DOI: 10.1038/s41592-023-02151-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/12/2023] [Indexed: 02/15/2024]
Abstract
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.
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Affiliation(s)
- Lena Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
- Medical Faculty, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
| | - Annika Reinke
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Patrick Godau
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Minu D Tizabi
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Florian Buettner
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Frankfurt am Main, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Department of Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany
- Department of Informatics, Goethe University Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Insititute, Frankfurt am Main, Germany
| | - Evangelia Christodoulou
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Ben Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine, University Medicine Essen, Essen, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis and Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
| | - Manuel Wiesenfarth
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - A Emre Kavur
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Michael Baumgartner
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Matthias Eisenmann
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Doreen Heckmann-Nötzel
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Tim Rädsch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany
| | - Laura Acion
- Instituto de Cálculo, CONICET - Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Tal Arbel
- Centre for Intelligent Machines and MILA (Québec Artificial Intelligence Institute), McGill University, Montréal, Quebec, Canada
| | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, IU Health Information and Translational Sciences Building, Indianapolis, IN, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Arriel Benis
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel
- European Federation for Medical Informatics, Le Mont-sur-Lausanne, Switzerland
| | - Matthew B Blaschko
- Center for Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Veronika Cheplygina
- Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gary S Collins
- Centre for Statistics in Medicine, University of Oxford, Nuffield Orthopaedic Centre, Oxford, UK
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
| | - Luciana Ferrer
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Adrian Galdran
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain
- Australian Institute for Machine Learning AIML, University of Adelaide, Adelaide, South Australia, Australia
| | - Bram van Ginneken
- Fraunhofer MEVIS, Bremen, Germany
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Robert Haase
- Technische Universität (TU) Dresden, DFG Cluster of Excellence 'Physics of Life', Dresden, Germany
- Center for Systems Biology, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig University, Leipzig, Germany
| | - Daniel A Hashimoto
- Department of Surgery, Perelman School of Medicine, Philadelphia, PA, USA
- General Robotics Automation Sensing and Perception Laboratory, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Pierre Jannin
- Laboratoire Traitement du Signal et de l'Image - UMR_S 1099, Université de Rennes 1, Rennes, France
- INSERM, Paris, France
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dagmar Kainmueller
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Biomedical Image Analysis and HI Helmholtz Imaging, Berlin, Germany
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Bernhard Kainz
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
- Department AIBE, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany
| | | | | | | | - Annette Kopp-Schneider
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Health Science Center, Stony Brook, NY, USA
| | | | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Amin Madani
- Department of Surgery, University Health Network, Philadelphia, PA, USA
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne L Martel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Peter Mattson
- Google, 1600 Amphitheatre Pkwy, Mountain View, CA, USA
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, UNSW Sydney, Kensington, New South Wales, Australia
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
- Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Brennan Nichyporuk
- MILA (Québec Artificial Intelligence Institute), Montréal, Quebec, Canada
| | - Felix Nickel
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Petersen
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Nasir Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, UK
| | | | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Clara I Sánchez
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Abdel A Taha
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
| | - Aleksei Tiulpin
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Neurocenter Oulu, Oulu University Hospital, Oulu, Finland
| | | | - Ben Van Calster
- Department of Development and Regeneration and EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Gaël Varoquaux
- Parietal project team, INRIA Saclay-Île de France, Palaiseau, France
| | - Paul F Jäger
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group, Heidelberg, Germany.
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3
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Kopp-Schneider A, Wiesenfarth M, Held L, Calderazzo S. Simulating and reporting frequentist operating characteristics of clinical trials that borrow external information: Towards a fair comparison in case of one-arm and hybrid control two-arm trials. Pharm Stat 2024; 23:4-19. [PMID: 37632266 DOI: 10.1002/pst.2334] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/25/2023] [Accepted: 08/01/2023] [Indexed: 08/27/2023]
Abstract
Borrowing information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often performed in small patient cohorts for practical or ethical reasons. Even though methods proposed for borrowing from external data are mainly based on Bayesian approaches that incorporate external information into the prior for the current analysis, frequentist operating characteristics of the analysis strategy are often of interest. In particular, type I error rate and power at a prespecified point alternative are the focus. We propose a procedure to investigate and report the frequentist operating characteristics in this context. The approach evaluates type I error rate of the test with borrowing from external data and calibrates the test without borrowing to this type I error rate. On this basis, a fair comparison of power between the test with and without borrowing is achieved. We show that no power gains are possible in one-sided one-arm and two-arm hybrid control trials with normal endpoint, a finding proven in general before. We prove that in one-arm fixed-borrowing situations, unconditional power (i.e., when external data is random) is reduced. The Empirical Bayes power prior approach that dynamically borrows information according to the similarity of current and external data avoids the exorbitant type I error inflation occurring with fixed borrowing. In the hybrid control two-arm trial we observe power reductions as compared to the test calibrated to borrowing that increase when considering unconditional power.
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Affiliation(s)
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Leonhard Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Silvia Calderazzo
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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4
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Calderazzo S, Wiesenfarth M, Kopp-Schneider A. Robust incorporation of historical information with known type I error rate inflation. Biom J 2024; 66:e2200322. [PMID: 38063813 DOI: 10.1002/bimj.202200322] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 07/30/2023] [Accepted: 09/23/2023] [Indexed: 01/30/2024]
Abstract
Bayesian clinical trials can benefit from available historical information through the specification of informative prior distributions. Concerns are however often raised about the potential for prior-data conflict and the impact of Bayes test decisions on frequentist operating characteristics, with particular attention being assigned to inflation of type I error (TIE) rates. This motivates the development of principled borrowing mechanisms, that strike a balance between frequentist and Bayesian decisions. Ideally, the trust assigned to historical information defines the degree of robustness to prior-data conflict one is willing to sacrifice. However, such relationship is often not directly available when explicitly considering inflation of TIE rates. We build on available literature relating frequentist and Bayesian test decisions, and investigate a rationale for inflation of TIE rate which explicitly and linearly relates the amount of borrowing and the amount of TIE rate inflation in one-arm studies. A novel dynamic borrowing mechanism tailored to hypothesis testing is additionally proposed. We show that, while dynamic borrowing prevents the possibility to obtain a simple closed-form TIE rate computation, an explicit upper bound can still be enforced. Connections with the robust mixture prior approach, particularly in relation to the choice of the mixture weight and robust component, are made. Simulations are performed to show the properties of the approach for normal and binomial outcomes, and an exemplary application is demonstrated in a case study.
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Affiliation(s)
- Silvia Calderazzo
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
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5
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Ayala L, Adler TJ, Seidlitz S, Wirkert S, Engels C, Seitel A, Sellner J, Aksenov A, Bodenbach M, Bader P, Baron S, Vemuri A, Wiesenfarth M, Schreck N, Mindroc D, Tizabi M, Pirmann S, Everitt B, Kopp-Schneider A, Teber D, Maier-Hein L. Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery. Sci Adv 2023; 9:eadd6778. [PMID: 36897951 PMCID: PMC10005169 DOI: 10.1126/sciadv.add6778] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. We developed a laparoscopic real-time multispectral imaging system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional surgical view of the patient with functional information at a video rate of 25 Hz. To enable contrast agent-free ischemia monitoring during laparoscopic partial nephrectomy, we phrase the problem of ischemia detection as an out-of-distribution detection problem that does not rely on data from any other patient and uses an ensemble of invertible neural networks at its core. An in-human trial demonstrates the feasibility of our approach and highlights the potential of spectral imaging combined with advanced deep learning-based analysis tools for fast, efficient, reliable, and safe functional laparoscopic imaging.
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Affiliation(s)
- Leonardo Ayala
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Tim J. Adler
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Silvia Seidlitz
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - Sebastian Wirkert
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Alexander Seitel
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jan Sellner
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | | | | | - Pia Bader
- Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | | | - Anant Vemuri
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nicholas Schreck
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Diana Mindroc
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Minu Tizabi
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian Pirmann
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Brittaney Everitt
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Dogu Teber
- Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Lena Maier-Hein
- Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty, Heidelberg University, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
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6
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Roß T, Bruno P, Reinke A, Wiesenfarth M, Koeppel L, Full PM, Pekdemir B, Godau P, Trofimova D, Isensee F, Adler TJ, Tran TN, Moccia S, Calimeri F, Müller-Stich BP, Kopp-Schneider A, Maier-Hein L. Beyond rankings: Learning (more) from algorithm validation. Med Image Anal 2023; 86:102765. [PMID: 36965252 DOI: 10.1016/j.media.2023.102765] [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: 06/17/2021] [Revised: 05/24/2022] [Accepted: 02/08/2023] [Indexed: 03/06/2023]
Abstract
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond.
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Affiliation(s)
- Tobias Roß
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Pierangela Bruno
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Annika Reinke
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lisa Koeppel
- Section Clinical Tropical Medicine, Heidelberg University, Heidelberg, Germany
| | - Peter M Full
- Medical Faculty, Heidelberg University, Heidelberg, Germany; Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Bünyamin Pekdemir
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Patrick Godau
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Germany
| | - Darya Trofimova
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; HIP Applied Computer Vision Lab, MIC, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fabian Isensee
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany; HIP Applied Computer Vision Lab, MIC, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tim J Adler
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thuy N Tran
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Beat P Müller-Stich
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Lena Maier-Hein
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Germany; Germany and National Center for Tumor Diseases (NCT), Heidelberg, Germany
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7
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Nikitina E, Burk-Körner A, Wiesenfarth M, Alwers E, Heide D, Tessmer C, Ernst C, Krunic D, Schrotz-King P, Chang-Claude J, von Winterfeld M, Herpel E, Brobeil A, Brenner H, Heikenwalder M, Hoffmeister M, Kopp-Schneider A, Bund T. Bovine meat and milk factor protein expression in tumor-free mucosa of colorectal cancer patients coincides with macrophages and might interfere with patient survival. Mol Oncol 2023. [PMID: 36811271 DOI: 10.1002/1878-0261.13390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 12/05/2022] [Accepted: 02/03/2023] [Indexed: 02/24/2023] Open
Abstract
Bovine milk and meat factors (BMMFs) are plasmid-like DNA molecules isolated from bovine milk and serum, as well as the peritumor of colorectal cancer (CRC) patients. BMMFs have been proposed as zoonotic infectious agents and drivers of indirect carcinogenesis of CRC, inducing chronic tissue inflammation, radical formation and increased levels of DNA damage. Data on expression of BMMFs in large clinical cohorts to test an association with co-markers and clinical parameters were not previously available and were therefore assessed in this study. Tissue sections with paired tumor-adjacent mucosa and tumor tissues of CRC patients [individual cohorts and tissue microarrays (TMAs) (n = 246)], low-/high-grade dysplasia (LGD/HGD) and mucosa of healthy donors were used for immunohistochemical quantification of the expression of BMMF replication protein (Rep) and CD68/CD163 (macrophages) by co-immunofluorescence microscopy and immunohistochemical scoring (TMA). Rep was expressed in the tumor-adjacent mucosa of 99% of CRC patients (TMA), was histologically associated with CD68+ /CD163+ macrophages and was increased in CRC patients when compared to healthy controls. Tumor tissues showed only low stromal Rep expression. Rep was expressed in LGD and less in HGD but was strongly expressed in LGD/HGD-adjacent tissues. Albeit not reaching statistical significance, incidence curves for CRC-specific death were increased for higher Rep expression (TMA), with high tumor-adjacent Rep expression being linked to the highest incidence of death. BMMF Rep expression might represent a marker and early risk factor for CRC. The correlation between Rep and CD68 expression supports a previous hypothesis that BMMF-specific inflammatory regulations, including macrophages, are involved in the pathogenesis of CRC.
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Affiliation(s)
- Ekaterina Nikitina
- Division of Episomal-persistent DNA in Cancer- and Chronic Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Amelie Burk-Körner
- Division of Episomal-persistent DNA in Cancer- and Chronic Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elizabeth Alwers
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Danijela Heide
- Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Claudia Tessmer
- Monoclonal Antibody Unit of the Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Claudia Ernst
- Division of Episomal-persistent DNA in Cancer- and Chronic Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Damir Krunic
- Light Microscopy Facility, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Petra Schrotz-King
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Jenny Chang-Claude
- Unit of Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg, Hamburg, Germany
| | - Moritz von Winterfeld
- Institute of Pathology, University Hospital Heidelberg, Germany
- Pathologie Rosenheim, Germany
| | - Esther Herpel
- Institute of Pathology, University Hospital Heidelberg, Germany
- Tissue Bank of the National Center for Tumor Diseases (NCT) Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, Germany
- Tissue Bank of the National Center for Tumor Diseases (NCT) Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Mathias Heikenwalder
- Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Timo Bund
- Division of Episomal-persistent DNA in Cancer- and Chronic Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany
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8
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Saner YM, Wiesenfarth M, Weru V, Ladyzhensky B, Tschirdewahn S, Püllen L, Bonekamp D, Reis H, Krafft U, Heß J, Kesch C, Darr C, Forsting M, Wetter A, Umutlu L, Haubold J, Hadaschik B, Radtke JP. Detection of Clinically Significant Prostate Cancer Using Targeted Biopsy with Four Cores Versus Target Saturation Biopsy with Nine Cores in Transperineal Prostate Fusion Biopsy: A Prospective Randomized Trial. Eur Urol Oncol 2023; 6:49-55. [PMID: 36175281 DOI: 10.1016/j.euo.2022.08.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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: 12/26/2021] [Revised: 08/04/2022] [Accepted: 08/29/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Multiparametric magnetic resonance imaging (mpMRI) and targeted biopsy (TB) facilitate accurate detection of clinically significant prostate cancer (csPC). However, it remains unclear how targeted cores should be applied for accurate diagnosis of csPC. OBJECTIVE To assess csPC detection rates for two target-directed MRI/transrectal ultrasonography (TRUS) fusion biopsy approaches, conventional TB and target saturation biopsy (TS). DESIGN, SETTING, AND PARTICIPANTS This was a prospective single-center study of outcomes for transperineal MRI/TRUS fusion biopsies for 170 men. Half of the men (n = 85) were randomized to conventional TB with four cores per lesion and half (n = 85) to TS with nine cores. Biopsies were performed by three experienced board-certified urologists. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS PC and csPC (International Society of Urological Pathology grade group ≥2) detection rates for systematic biopsy (SB), TB, and TS were analyzed using McNemar's test for intrapatient comparisons and Fisher's exact test for TS versus TB. A combination of targeted biopsy (TS or TB) and SB served as the reference. RESULTS AND LIMITATIONS According to the reference, csPC was diagnosed for 57 men in the TS group and 36 men in the TB group. Of these, TS detected 57/57 csPC cases and TB detected 33/36 csPC cases (p = 0.058). Detection of Gleason grade group 1 disease was 10/12 cases with TS and 8/17 cases with TB (p = 0.055). In addition, TS detected 97% of 63 csPC lesions, compared to 86% with TB (p = 0.1). Limitations include the single-center design, the limited generalizability owing to the transperineal biopsy route, the lack of central review of pathology and radical prostatectomy correlation, and uneven distributions of csPC prevalence, Prostate Imaging-Reporting and Data System (PI-RADS) 5 lesions, men with two or more PI-RADS ≥3 lesions, and prostate-specific antigen density between the groups, which may have affected the results. CONCLUSIONS In our study, rates of csPC detection did not significantly differ between TS and TB. PATIENT SUMMARY In this study, we investigated two targeted approaches for taking prostate biopsy samples after observation of suspicious lesions on prostate scans. We found that the rates of detection of prostate cancer did not significantly differ between the two approaches.
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Affiliation(s)
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Vivienn Weru
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Boris Ladyzhensky
- Department of Anesthesia and Perioperative Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Lukas Püllen
- Department of Urology, University Hospital Essen, Essen, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Henning Reis
- Institute of Pathology, University Duisburg-Essen, Essen, Germany
| | - Ulrich Krafft
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Jochen Heß
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Claudia Kesch
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Christopher Darr
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Axel Wetter
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Boris Hadaschik
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Jan Philipp Radtke
- Department of Urology, University Hospital Essen, Essen, Germany; Department of Radiology, German Cancer Research Center, Heidelberg, Germany.
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9
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Antonelli M, Reinke A, Bakas S, Farahani K, Kopp-Schneider A, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM, van Ginneken B, Bilello M, Bilic P, Christ PF, Do RKG, Gollub MJ, Heckers SH, Huisman H, Jarnagin WR, McHugo MK, Napel S, Pernicka JSG, Rhode K, Tobon-Gomez C, Vorontsov E, Meakin JA, Ourselin S, Wiesenfarth M, Arbeláez P, Bae B, Chen S, Daza L, Feng J, He B, Isensee F, Ji Y, Jia F, Kim I, Maier-Hein K, Merhof D, Pai A, Park B, Perslev M, Rezaiifar R, Rippel O, Sarasua I, Shen W, Son J, Wachinger C, Wang L, Wang Y, Xia Y, Xu D, Xu Z, Zheng Y, Simpson AL, Maier-Hein L, Cardoso MJ. The Medical Segmentation Decathlon. Nat Commun 2022; 13:4128. [PMID: 35840566 PMCID: PMC9287542 DOI: 10.1038/s41467-022-30695-9] [Citation(s) in RCA: 115] [Impact Index Per Article: 57.5] [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: 08/16/2021] [Accepted: 05/13/2022] [Indexed: 02/05/2023] Open
Abstract
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.
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Affiliation(s)
- Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
| | - Annika Reinke
- Div. Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany.,HI Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Mathematics and Computer Science, University of Heidelberg, Heidelberg, Germany
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NIH), Bethesda, MD, USA
| | | | - Bennett A Landman
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Geert Litjens
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Bjoern Menze
- Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | | | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center (NIH), Bethesda, MD, USA
| | - Bram van Ginneken
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Patrick Bilic
- Department of Informatics, Technische Universität München, München, Germany
| | - Patrick F Christ
- Department of Informatics, Technische Universität München, München, Germany
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc J Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Stephan H Heckers
- Department of Psychiatry & Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Henkjan Huisman
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - William R Jarnagin
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maureen K McHugo
- Department of Psychiatry & Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sandy Napel
- Department of Radiology, Stanford University, Stanford, CA, USA
| | | | - Kawal Rhode
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Catalina Tobon-Gomez
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Eugene Vorontsov
- Department of Computer Science and Software Engineering, École Polytechnique de Montréal, Montréal, QC, Canada
| | - James A Meakin
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Manuel Wiesenfarth
- Div. Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | | | - Laura Daza
- Universidad de los Andes, Bogota, Colombia
| | - Jianjiang Feng
- Department of Automation, Tsinghua University, Beijing, China
| | - Baochun He
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Fabian Isensee
- HI Applied Computer Vision Lab, Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Yuanfeng Ji
- Department of Computer Science, Xiamen University, Xiamen, China
| | - Fucang Jia
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ildoo Kim
- Kakao Brain, Seongnam-si, Republic of Korea
| | - Klaus Maier-Hein
- Cerebriu A/S, Copenhagen, Denmark.,Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Dorit Merhof
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Akshay Pai
- Cerebriu A/S, Copenhagen, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | | | - Mathias Perslev
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | | | - Oliver Rippel
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Ignacio Sarasua
- Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, University Hospital, LMU München, Germany
| | - Wei Shen
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | | | - Christian Wachinger
- Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, University Hospital, LMU München, Germany
| | - Liansheng Wang
- Department of Computer Science, Xiamen University, Xiamen, China
| | - Yan Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
| | - Yingda Xia
- Johns Hopkins University, Baltimore, MD, USA
| | | | - Zhanwei Xu
- Department of Automation, Tsinghua University, Beijing, China
| | | | - Amber L Simpson
- School of Computing/Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Lena Maier-Hein
- Div. Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany.,HI Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Mathematics and Computer Science, University of Heidelberg, Heidelberg, Germany.,Medical Faculty, University of Heidelberg, Heidelberg, Germany
| | - M Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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10
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Zocholl D, Wiesenfarth M, Rauch G, Kopp-Schneider A. On the feasibility of pediatric dose-finding trials in small samples with information from a preceding trial in adults. J Biopharm Stat 2021; 32:652-670. [PMID: 34962850 DOI: 10.1080/10543406.2021.2011905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
We consider the case of pediatric dose-finding trials with extremely limited sample size. The operating characteristics of the standard design, the Continual Reassessment Method (CRM), are only well described for sample sizes of about 20 patients or more. In this simulation study, we assume the situation of a pediatric trial with only 10 patients and a preceding dose-finding trial in adults. Based on the adult data, we reduce the set of pediatric doses and formulate (partially) informative prior distributions for the pediatric trial. Our simulations show that such small pediatric dose-finding trials with robustified priors may provide sufficient operating characteristics.
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11
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Zhang KS, Schelb P, Kohl S, Radtke JP, Wiesenfarth M, Schimmöller L, Kuder TA, Stenzinger A, Hohenfellner M, Schlemmer HP, Maier-Hein K, Bonekamp D. Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or mean ADC. Magn Reson Imaging 2021; 82:9-17. [PMID: 34147597 DOI: 10.1016/j.mri.2021.06.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 12/11/2020] [Revised: 05/08/2021] [Accepted: 06/15/2021] [Indexed: 10/21/2022]
Abstract
Background Currently, interpretation of prostate MRI is performed qualitatively. Quantitative assessment of the mean apparent diffusion coefficient (mADC) is promising to improve diagnostic accuracy while radiomic machine learning (RML) allows to probe complex parameter spaces to identify the most promising multi-parametric models. We have previously developed quantitative RML and ADC classifiers for prediction of clinically significant prostate cancer (sPC) from prostate MRI, however these have not been combined with radiologist PI-RADS assessment. Purpose To propose and evaluate diagnostic algorithms combining quantitative ADC or RML and qualitative PI-RADS assessment for prediction of sPC. Methods and population The previously published quantitative models (RML and mADC) were utilized to construct four algorithms: 1) Down(ADC) and 2) Down(RML): clinically detected PI-RADS positive prostate lesions (defined as either PI-RADS≥3 or ≥4) were downgraded to MRI negative upon negative quantitative assessment; and 3) Up(ADC) and 4) Up(RML): MRI-negative lesions were upgraded to MRI-positive upon positive assessment of quantitative parameters. Analyses were performed at the individual lesion level and the patient level in 133 consecutive patients with suspicion for clinically significant prostate cancer (sPC, International Society of Urological Pathology (ISUP) grade group≥2), the test set subcohort of a previously published patient population. McNemar test was used to compare differences in sensitivity, specificity and accuracy. Differences between lesions of different prostate zones were assessed using ANOVA. Reduction in false positive assessments was assessed as ratios. Results Compared to clinical assessment at the PI-RADS≥4 cut-off alone, algorithms Down(ADC/RML) improved specificity from 43% to 65% (p = 0.001)/62% (p = 0.003), while sensitivity did not change significantly at 89% compared to 87% (p = 1.0)/89% (unchanged) on the patient level. Reduction of false positive lesions was 50% [26/52] in the PZ and 53% [15/28] in the TZ. Algorithms Up(ADC/RML) led, on a patient basis, to an unfavorable loss of specificity from 43% to 30% (p = 0.039)/32% (p = 0.106), with insignificant increase of sensitivity from 89% to 96%/96% (both p = 1.0). Compared to clinical assessment at the PI-RADS≥3 cut-off alone, similar results were observed for Down(ADC) with significantly increased specificity from 2% to 23% (p < 0.001) and unchanged sensitivity on the lesion level; patient level specificity increased only non-significantly. Conclusion Downgrading PI-RADS≥3 and ≥ 4 lesions based on quantitative mADC measurements or RML classifiers can increase diagnostic accuracy by enhancing specificity and preserving sensitivity for detection of sPC and reduce false positives.
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Affiliation(s)
- Kevin Sun Zhang
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Patrick Schelb
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Heidelberg University Medical School, Heidelberg, Germany
| | - Simon Kohl
- Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jan Philipp Radtke
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lars Schimmöller
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany
| | - Tristan Anselm Kuder
- Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Markus Hohenfellner
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Germany
| | - Klaus Maier-Hein
- Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Germany
| | - David Bonekamp
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Germany.
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12
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Saner Y, Wiesenfarth M, Tschirdewahn S, Püllen L, Bonekamp D, Krafft U, Kesch C, Darr C, Forsting M, Umutlu L, Hadaschik B, Radtke J. Detection of significant prostate cancer using target saturation in transperineal magnetic resonance imaging/transrectal ultrasonography–fusion biopsy: A prospective, randomized comparison to conventional target biopsy. Eur Urol 2021. [DOI: 10.1016/s0302-2838(21)01281-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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13
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Wiesenfarth M, Reinke A, Landman BA, Eisenmann M, Saiz LA, Cardoso MJ, Maier-Hein L, Kopp-Schneider A. Methods and open-source toolkit for analyzing and visualizing challenge results. Sci Rep 2021; 11:2369. [PMID: 33504883 PMCID: PMC7841186 DOI: 10.1038/s41598-021-82017-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 01/11/2021] [Indexed: 01/12/2023] Open
Abstract
Grand challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of these international competitions is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the contribution of this paper is two-fold: (1) we present a set of methods to comprehensively analyze and visualize the results of single-task and multi-task challenges and apply them to a number of simulated and real-life challenges to demonstrate their specific strengths and weaknesses; (2) we release the open-source framework challengeR as part of this work to enable fast and wide adoption of the methodology proposed in this paper. Our approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. This is demonstrated by the experiments performed in the specific context of biomedical image analysis challenges. Our framework could thus become an important tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond.
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Affiliation(s)
- Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg, 69120, Germany.
| | - Annika Reinke
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, TN, 37235-1679, USA
| | - Matthias Eisenmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Laura Aguilera Saiz
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, WC2R 2LS, UK
| | - Lena Maier-Hein
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, Germany.
| | - Annette Kopp-Schneider
- Division of Biostatistics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg, 69120, Germany
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14
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Roß T, Reinke A, Full PM, Wagner M, Kenngott H, Apitz M, Hempe H, Mindroc-Filimon D, Scholz P, Tran TN, Bruno P, Arbeláez P, Bian GB, Bodenstedt S, Bolmgren JL, Bravo-Sánchez L, Chen HB, González C, Guo D, Halvorsen P, Heng PA, Hosgor E, Hou ZG, Isensee F, Jha D, Jiang T, Jin Y, Kirtac K, Kletz S, Leger S, Li Z, Maier-Hein KH, Ni ZL, Riegler MA, Schoeffmann K, Shi R, Speidel S, Stenzel M, Twick I, Wang G, Wang J, Wang L, Wang L, Zhang Y, Zhou YJ, Zhu L, Wiesenfarth M, Kopp-Schneider A, Müller-Stich BP, Maier-Hein L. Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge. Med Image Anal 2020; 70:101920. [PMID: 33676097 DOI: 10.1016/j.media.2020.101920] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [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: 05/20/2020] [Revised: 09/22/2020] [Accepted: 11/24/2020] [Indexed: 12/27/2022]
Abstract
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).
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Affiliation(s)
- Tobias Roß
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany; University of Heidelberg, Germany, Seminarstraße 2, 69117 Heidelberg, Germany.
| | - Annika Reinke
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany; University of Heidelberg, Germany, Seminarstraße 2, 69117 Heidelberg, Germany
| | - Peter M Full
- University of Heidelberg, Germany, Seminarstraße 2, 69117 Heidelberg, Germany; Division of Medical Image Computing (MIC), Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Martin Wagner
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Hannes Kenngott
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Martin Apitz
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Hellena Hempe
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Diana Mindroc-Filimon
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Patrick Scholz
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany; HIDSS4Health - Helmholtz Information and Data Science School for Health, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Thuy Nuong Tran
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Pierangela Bruno
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany; Department of Mathematics and Computer Science, University of Calabria, 87036 Rende, Italy
| | - Pablo Arbeláez
- Universidad de los Andes, Cra. 1 No 18A - 12, 111711 Bogotá, Colombia
| | - Gui-Bin Bian
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | - Sebastian Bodenstedt
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany; Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Bautzner Landstraße 400, 01328 Dresden, Germany
| | | | | | - Hua-Bin Chen
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | - Cristina González
- Universidad de los Andes, Cra. 1 No 18A - 12, 111711 Bogotá, Colombia
| | - Dong Guo
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Shahe Campus:No.4, Section 2, North Jianshe Road, 610054
- Qingshuihe Campus:No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Pål Halvorsen
- SimulaMet, Pilestredet 52, 0167 Oslo, Norway; Oslo Metropolitan University (OsloMet), Pilestredet 52, 0167 Oslo, Norway
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Chung Chi Rd, Ma Liu Shui, Hong Kong, China
| | - Enes Hosgor
- caresyntax, Komturstraße 18A, 12099 Berlin, Germany
| | - Zeng-Guang Hou
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | - Fabian Isensee
- University of Heidelberg, Germany, Seminarstraße 2, 69117 Heidelberg, Germany; Division of Medical Image Computing (MIC), Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Debesh Jha
- SimulaMet, Pilestredet 52, 0167 Oslo, Norway; Department of Informatics, UIT The Arctic University of Norway, Hansine Hansens vei 54, 9037 Tromsø, Norway
| | - Tingting Jiang
- Institute of Digital Media (NELVT), Peking University, 5 Yiheyuan Rd, Haidian District, 100871 Peking, China
| | - Yueming Jin
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Chung Chi Rd, Ma Liu Shui, Hong Kong, China
| | - Kadir Kirtac
- caresyntax, Komturstraße 18A, 12099 Berlin, Germany
| | - Sabrina Kletz
- Institute of Information Technology, Klagenfurt University, Universitätsstraße 65-67, 9020 Klagenfurt, Austria
| | - Stefan Leger
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany; Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Bautzner Landstraße 400, 01328 Dresden, Germany
| | - Zhixuan Li
- Institute of Digital Media (NELVT), Peking University, 5 Yiheyuan Rd, Haidian District, 100871 Peking, China
| | - Klaus H Maier-Hein
- Division of Medical Image Computing (MIC), Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Zhen-Liang Ni
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | | | - Klaus Schoeffmann
- Institute of Information Technology, Klagenfurt University, Universitätsstraße 65-67, 9020 Klagenfurt, Austria
| | - Ruohua Shi
- Institute of Digital Media (NELVT), Peking University, 5 Yiheyuan Rd, Haidian District, 100871 Peking, China
| | - Stefanie Speidel
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany; Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Bautzner Landstraße 400, 01328 Dresden, Germany
| | | | | | - Gutai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Shahe Campus:No.4, Section 2, North Jianshe Road, 610054
- Qingshuihe Campus:No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Jiacheng Wang
- Department of Computer Science, School of Informatics, Xiamen University, 422 Siming South Road, 361005 Xiamen, China
| | - Liansheng Wang
- Department of Computer Science, School of Informatics, Xiamen University, 422 Siming South Road, 361005 Xiamen, China
| | - Lu Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Shahe Campus:No.4, Section 2, North Jianshe Road, 610054
- Qingshuihe Campus:No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Yujie Zhang
- Department of Computer Science, School of Informatics, Xiamen University, 422 Siming South Road, 361005 Xiamen, China
| | - Yan-Jie Zhou
- University of Chinese Academy Sciences, 52 Sanlihe Rd., Beijing, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100864 Beijing, China
| | - Lei Zhu
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Chung Chi Rd, Ma Liu Shui, Hong Kong, China
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, Germany
| | - Annette Kopp-Schneider
- Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, Germany
| | - Beat P Müller-Stich
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Lena Maier-Hein
- Computer Assisted Medical Interventions (CAMI), German Cancer Research Center, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
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15
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Darr C, Finis F, Wiesenfarth M, Giganti F, Tschirdewahn S, Krafft U, Kesch C, Bonekamp D, Forsting M, Wetter A, Reis H, Hadaschik BA, Haubold J, Radtke JP. Three-dimensional Magnetic Resonance Imaging-based Printed Models of Prostate Anatomy and Targeted Biopsy-proven Index Tumor to Facilitate Patient-tailored Radical Prostatectomy-A Feasibility Study. Eur Urol Oncol 2020; 5:357-361. [PMID: 32873530 DOI: 10.1016/j.euo.2020.08.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [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: 05/27/2020] [Revised: 07/22/2020] [Accepted: 08/10/2020] [Indexed: 12/15/2022]
Abstract
In this prospective single-center feasibility study, we demonstrate that the use of three-dimensional (3D)-printed prostate models support nerve-sparing radical prostatectomy (RP) and intraoperative frozen sectioning (IFS) in ten men suffering from intermediate- and high-risk prostate cancer (PC), of whom seven harbored pT3 disease. Patient-specific 3D resin models were printed based on preoperative multiparametric magnetic resonance imaging (mpMRI) to provide an exact 3D impression of significant tumor lesions. RP and IFS were planned in a patient-tailored fashion. The 36-region Prostate Imaging Reporting and Data System (PI-RADS) v2.0 scheme was used to compare the MRI/3D print with whole-mount histopathology. In all cases, localization of the index lesion was correctly displayed by MRI and the 3D model. Localization of significant PC lesions correlated significantly (Pearson`s correlation coefficient of 0.88; p < 0.001). In addition, a significant correlation of the width, length, and volume of the tumor and prostate gland, derived from the printed model and histopathology, was found, using Pearson's correlation analyses and Bland-Altman plots. In conclusion, 3D-printed prostate models correlate well with final pathology and can be used to tailor RP. PATIENT SUMMARY: The use of three-dimensional (3D)-printed prostate models based on preoperative magnetic resonance imaging (MRI) may improve prostatectomy outcome. This study confirmed the accuracy of 3D-printed prostates compared with pathology from radical prostatectomy specimens. Thus, MRI-derived 3D-printed prostate models can assist in prostate cancer surgery.
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Affiliation(s)
- Christopher Darr
- Department of Urology, University Hospital Essen, Essen, Germany; German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Friederike Finis
- Department of Urology, University Hospital Essen, Essen, Germany; German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Francesco Giganti
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK; Division of Surgery and Interventional Science, University College London, London, UK
| | - Stephan Tschirdewahn
- Department of Urology, University Hospital Essen, Essen, Germany; German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Ulrich Krafft
- Department of Urology, University Hospital Essen, Essen, Germany; German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Claudia Kesch
- Department of Urology, University Hospital Essen, Essen, Germany; German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Forsting
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany; Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Axel Wetter
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany; Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Henning Reis
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany; Institute of Pathology, University Hospital Essen, Essen, Germany
| | - Boris A Hadaschik
- Department of Urology, University Hospital Essen, Essen, Germany; German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany; Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Jan Philipp Radtke
- Department of Urology, University Hospital Essen, Essen, Germany; German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany; Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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16
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Radtke JP, Nyarangi-Dix J, Wiesenfarth M, Hadaschik B. Re: The Key Combined Value of Multiparametric Magnetic Resonance Imaging, and Magnetic Resonance Imaging-targeted and Concomitant Systematic Biopsies for the Prediction of Adverse Pathological Features in Prostate Cancer Patients Undergoing Radical Prostatectomy. Eur Urol 2020; 79:164-165. [PMID: 32847701 DOI: 10.1016/j.eururo.2020.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 08/13/2020] [Indexed: 10/23/2022]
Affiliation(s)
- Jan Philipp Radtke
- Department of Urology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Department of Radiology, German Cancer Research Center.
| | | | - Manuel Wiesenfarth
- Department of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Boris Hadaschik
- Department of Urology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
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17
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Schelb P, Wang X, Radtke JP, Wiesenfarth M, Kickingereder P, Stenzinger A, Hohenfellner M, Schlemmer HP, Maier-Hein KH, Bonekamp D. Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment. Eur Radiol 2020; 31:302-313. [PMID: 32767102 PMCID: PMC7755653 DOI: 10.1007/s00330-020-07086-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [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: 03/19/2020] [Revised: 06/03/2020] [Accepted: 07/20/2020] [Indexed: 01/12/2023]
Abstract
Objectives To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI. Methods In 2017, 284 consecutive men in active surveillance, biopsy-naïve or pre-biopsied, received targeted and extended systematic MRI/transrectal US-fusion biopsy, after examination on a single MRI scanner (3 T). A prospective adjustment scheme was evaluated comparing the performance of the Prostate Imaging Reporting and Data System (PI-RADS) and U-Net using sensitivity, specificity, predictive values, and the Dice coefficient. Results In the 259 eligible men (median 64 [IQR 61–72] years), PI-RADS had a sensitivity of 98% [106/108]/84% [91/108] with a specificity of 17% [25/151]/58% [88/151], for thresholds at ≥ 3/≥ 4 respectively. U-Net using dynamic threshold adjustment had a sensitivity of 99% [107/108]/83% [90/108] (p > 0.99/> 0.99) with a specificity of 24% [36/151]/55% [83/151] (p > 0.99/> 0.99) for probability thresholds d3 and d4 emulating PI-RADS ≥ 3 and ≥ 4 decisions respectively, not statistically different from PI-RADS. Co-occurrence of a radiological PI-RADS ≥ 4 examination and U-Net ≥ d3 assessment significantly improved the positive predictive value from 59 to 63% (p = 0.03), on a per-patient basis. Conclusions U-Net has similar performance to PI-RADS in simulated continued clinical use. Regular quality assurance should be implemented to ensure desired performance. Key Points • U-Net maintained similar diagnostic performance compared to radiological assessment of PI-RADS ≥ 4 when applied in a simulated clinical deployment. • Application of our proposed prospective dynamic calibration method successfully adjusted U-Net performance within acceptable limits of the PI-RADS reference over time, while not being limited to PI-RADS as a reference. • Simultaneous detection by U-Net and radiological assessment significantly improved the positive predictive value on a per-patient and per-lesion basis, while the negative predictive value remained unchanged. Electronic supplementary material The online version of this article (10.1007/s00330-020-07086-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Patrick Schelb
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Xianfeng Wang
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.,Department of Radiology, Affiliated Hospital of Guilin Medical University, Guangxi, Guilin, People's Republic of China
| | - Jan Philipp Radtke
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.,Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Markus Hohenfellner
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Klaus H Maier-Hein
- German Cancer Consortium (DKTK), Heidelberg, Germany.,Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Bonekamp
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany. .,German Cancer Consortium (DKTK), Heidelberg, Germany.
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18
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Calderazzo S, Wiesenfarth M, Kopp-Schneider A. A decision-theoretic approach to Bayesian clinical trial design and evaluation of robustness to prior-data conflict. Biostatistics 2020; 23:328-344. [PMID: 32735010 PMCID: PMC9118338 DOI: 10.1093/biostatistics/kxaa027] [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: 09/17/2019] [Revised: 06/24/2020] [Accepted: 06/26/2020] [Indexed: 11/29/2022] Open
Abstract
Bayesian clinical trials allow taking advantage of relevant external information through the elicitation of prior distributions, which influence Bayesian posterior parameter estimates and test decisions. However, incorporation of historical information can have harmful consequences on the trial’s frequentist (conditional) operating characteristics in case of inconsistency between prior information and the newly collected data. A compromise between meaningful incorporation of historical information and strict control of frequentist error rates is therefore often sought. Our aim is thus to review and investigate the rationale and consequences of different approaches to relaxing strict frequentist control of error rates from a Bayesian decision-theoretic viewpoint. In particular, we define an integrated risk which incorporates losses arising from testing, estimation, and sampling. A weighted combination of the integrated risk addends arising from testing and estimation allows moving smoothly between these two targets. Furthermore, we explore different possible elicitations of the test error costs, leading to test decisions based either on posterior probabilities, or solely on Bayes factors. Sensitivity analyses are performed following the convention which makes a distinction between the prior of the data-generating process, and the analysis prior adopted to fit the data. Simulation in the case of normal and binomial outcomes and an application to a one-arm proof-of-concept trial, exemplify how such analysis can be conducted to explore sensitivity of the integrated risk, the operating characteristics, and the optimal sample size, to prior-data conflict. Robust analysis prior specifications, which gradually discount potentially conflicting prior information, are also included for comparison. Guidance with respect to cost elicitation, particularly in the context of a Phase II proof-of-concept trial, is provided.
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Affiliation(s)
- Silvia Calderazzo
- Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, 69120 Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, 69120 Heidelberg, Germany
| | - Annette Kopp-Schneider
- Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, 69120 Heidelberg, Germany
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19
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Tschirdewahn S, Wiesenfarth M, Bonekamp D, Püllen L, Reis H, Panic A, Kesch C, Darr C, Heß J, Giganti F, Moore CM, Guberina N, Forsting M, Wetter A, Hadaschik B, Radtke JP. Detection of Significant Prostate Cancer Using Target Saturation in Transperineal Magnetic Resonance Imaging/Transrectal Ultrasonography-fusion Biopsy. Eur Urol Focus 2020; 7:1300-1307. [PMID: 32660838 DOI: 10.1016/j.euf.2020.06.020] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [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: 04/06/2020] [Revised: 05/30/2020] [Accepted: 06/22/2020] [Indexed: 01/06/2023]
Abstract
BACKGROUND Multiparametric magnetic resonance imaging (mpMRI) and targeted biopsies (TBs) facilitate accurate detection of significant prostate cancer (sPC). However, it remains unclear how many cores should be applied per target. OBJECTIVE To assess sPC detection rates of two different target-dependent magnetic resonance imaging (MRI)/transrectal ultrasonography (TRUS)-fusion biopsy approaches (TB and target saturation [TS]) compared with extended systematic biopsies (SBs). DESIGN, SETTING, AND PARTICIPANTS Retrospective single-centre outcome of transperineal MRI/TRUS-fusion biopsies of 213 men was evaluated. All men underwent TB with a median of four cores per MRI lesion, followed by a median of 24 SBs, performed by experienced urologists. Cancer and sPC (International Society of Urological Pathology grade group ≥2) detection rates were analysed. TB was compared with SB and TS, with nine cores per target, calculated by the Ginsburg scheme and using individual cores of the lesion and its "penumbra". OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Cancer detection rates were calculated for TS, TB, and SB at both lesion and patient level. Combination of SB + TB served as a reference. Statistical differences in prostate cancer (PC) detection between groups were calculated using McNemar's tests with confidence intervals. RESULTS AND LIMITATIONS TS detected 99% of 134 sPC lesions, which was significantly higher than the detection by TB (87%, p = 0.001) and SB (82%, p < 0.001). SB detected significantly more of the 72 low-risk PC lesions than TB (99% vs 68%, p < 0.001) and 10% (p = 0.15) more than that detected by TS. At a per-patient level, 99% of men harbouring sPC were detected by TS. This was significantly higher than that by TB and SB (89%, p = 0.03 and 81%, p = 0.001, respectively). Limitations include limited generalisability, as a transperineal biopsy route was used. CONCLUSIONS TS detected significantly more cases of sPC than TB and extended SB. Given that both 99% of sPC lesions and men harbouring sPC were identified by TS, the results suggest that this approach allows to omit SB cores without compromising sPC detection. PATIENT SUMMARY Target saturation of magnetic resonance imaging-suspicious prostate lesions provides excellent cancer detection and finds fewer low-risk tumours than the current gold standard combination of targeted and systematic biopsies.
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Affiliation(s)
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lukas Püllen
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Henning Reis
- Institute of Pathology, University Duisburg-Essen, Essen, Germany
| | - Andrej Panic
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Claudia Kesch
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Christopher Darr
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Jochen Heß
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Francesco Giganti
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK; Division of Surgery and Interventional Science, University College London, London, UK
| | - Caroline M Moore
- Division of Surgery and Interventional Science, University College London, London, UK; Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Nika Guberina
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Axel Wetter
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Boris Hadaschik
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Jan Philipp Radtke
- Department of Urology, University Hospital Essen, Essen, Germany; Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Tschirdewahn S, Wiesenfarth M, Bonekamp D, Püllen L, Reis H, Panic A, Kesch C, Darr C, Giganti F, Forsting M, Wetter A, Hadaschik B, Radtke J. Detection of significant prostate cancer using target saturation in transperineal MRI/ TRUS-fusion biopsy. EUR UROL SUPPL 2020. [DOI: 10.1016/s2666-1683(20)33758-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Kopp‐Schneider A, Calderazzo S, Wiesenfarth M. Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control. Biom J 2020; 62:361-374. [PMID: 31265159 PMCID: PMC7079072 DOI: 10.1002/bimj.201800395] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 05/14/2019] [Accepted: 05/15/2019] [Indexed: 12/30/2022]
Abstract
In the era of precision medicine, novel designs are developed to deal with flexible clinical trials that incorporate many treatment strategies for multiple diseases in one trial setting. This situation often leads to small sample sizes in disease-treatment combinations and has fostered the discussion about the benefits of borrowing of external or historical information for decision-making in these trials. Several methods have been proposed that dynamically discount the amount of information borrowed from historical data based on the conformity between historical and current data. Specifically, Bayesian methods have been recommended and numerous investigations have been performed to characterize the properties of the various borrowing mechanisms with respect to the gain to be expected in the trials. However, there is common understanding that the risk of type I error inflation exists when information is borrowed and many simulation studies are carried out to quantify this effect. To add transparency to the debate, we show that if prior information is conditioned upon and a uniformly most powerful test exists, strict control of type I error implies that no power gain is possible under any mechanism of incorporation of prior information, including dynamic borrowing. The basis of the argument is to consider the test decision function as a function of the current data even when external information is included. We exemplify this finding in the case of a pediatric arm appended to an adult trial and dichotomous outcome for various methods of dynamic borrowing from adult information to the pediatric arm. In conclusion, if use of relevant external data is desired, the requirement of strict type I error control has to be replaced by more appropriate metrics.
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Affiliation(s)
| | - Silvia Calderazzo
- Division of BiostatisticsGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Manuel Wiesenfarth
- Division of BiostatisticsGerman Cancer Research Center (DKFZ)HeidelbergGermany
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Püllen L, Radtke JP, Wiesenfarth M, Roobol MJ, Verbeek JF, Wetter A, Guberina N, Pandey A, Hüttenbrink C, Tschirdewahn S, Pahernik S, Hadaschik BA, Distler FA. External validation of novel magnetic resonance imaging-based models for prostate cancer prediction. BJU Int 2019; 125:407-416. [DOI: 10.1111/bju.14958] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lukas Püllen
- Department of Urology; University Hospital Essen; Nordrhein-Westfalen Germany
| | - Jan P. Radtke
- Department of Urology; University Hospital Essen; Nordrhein-Westfalen Germany
- Department of Radiology; German Cancer Research Centre (DKFZ); Heidelberg Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics; German Cancer Research Centre (DKFZ); Heidelberg Germany
| | - Monique J. Roobol
- Department of Urology; Erasmus University Medical Centre; Rotterdam The Netherlands
| | - Jan F.M. Verbeek
- Department of Urology; Erasmus University Medical Centre; Rotterdam The Netherlands
| | - Axel Wetter
- Department of Radiology; University Hospital Essen; Nordrhein-Westfalen Germany
| | - Nika Guberina
- Department of Radiology; University Hospital Essen; Nordrhein-Westfalen Germany
| | - Abhishek Pandey
- Department of Urology; Paracelsus Medical University Nuremberg; Nürnberg Germany
| | - Clemens Hüttenbrink
- Department of Urology; Paracelsus Medical University Nuremberg; Nürnberg Germany
| | | | - Sascha Pahernik
- Department of Urology; Paracelsus Medical University Nuremberg; Nürnberg Germany
| | - Boris A. Hadaschik
- Department of Urology; University Hospital Essen; Nordrhein-Westfalen Germany
| | - Florian A. Distler
- Department of Urology; Paracelsus Medical University Nuremberg; Nürnberg Germany
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Schelb P, Kohl S, Radtke JP, Wiesenfarth M, Kickingereder P, Bickelhaupt S, Kuder TA, Stenzinger A, Hohenfellner M, Schlemmer HP, Maier-Hein KH, Bonekamp D. Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment. Radiology 2019; 293:607-617. [PMID: 31592731 DOI: 10.1148/radiol.2019190938] [Citation(s) in RCA: 170] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to provide diagnostic support for human interpretation requires further evaluation. Purpose To compare the performance of clinical assessment to a deep learning system optimized for segmentation trained with T2-weighted and diffusion MRI in the task of detection and segmentation of lesions suspicious for sPC. Materials and Methods In this retrospective study, T2-weighted and diffusion prostate MRI sequences from consecutive men examined with a single 3.0-T MRI system between 2015 and 2016 were manually segmented. Ground truth was provided by combined targeted and extended systematic MRI-transrectal US fusion biopsy, with sPC defined as International Society of Urological Pathology Gleason grade group greater than or equal to 2. By using split-sample validation, U-Net was internally validated on the training set (80% of the data) through cross validation and subsequently externally validated on the test set (20% of the data). U-Net-derived sPC probability maps were calibrated by matching sextant-based cross-validation performance to clinical performance of Prostate Imaging Reporting and Data System (PI-RADS). Performance of PI-RADS and U-Net were compared by using sensitivities, specificities, predictive values, and Dice coefficient. Results A total of 312 men (median age, 64 years; interquartile range [IQR], 58-71 years) were evaluated. The training set consisted of 250 men (median age, 64 years; IQR, 58-71 years) and the test set of 62 men (median age, 64 years; IQR, 60-69 years). In the test set, PI-RADS cutoffs greater than or equal to 3 versus cutoffs greater than or equal to 4 on a per-patient basis had sensitivity of 96% (25 of 26) versus 88% (23 of 26) at specificity of 22% (eight of 36) versus 50% (18 of 36). U-Net at probability thresholds of greater than or equal to 0.22 versus greater than or equal to 0.33 had sensitivity of 96% (25 of 26) versus 92% (24 of 26) (both P > .99) with specificity of 31% (11 of 36) versus 47% (17 of 36) (both P > .99), not statistically different from PI-RADS. Dice coefficients were 0.89 for prostate and 0.35 for MRI lesion segmentation. In the test set, coincidence of PI-RADS greater than or equal to 4 with U-Net lesions improved the positive predictive value from 48% (28 of 58) to 67% (24 of 36) for U-Net probability thresholds greater than or equal to 0.33 (P = .01), while the negative predictive value remained unchanged (83% [25 of 30] vs 83% [43 of 52]; P > .99). Conclusion U-Net trained with T2-weighted and diffusion MRI achieves similar performance to clinical Prostate Imaging Reporting and Data System assessment. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Padhani and Turkbey in this issue.
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Affiliation(s)
- Patrick Schelb
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Simon Kohl
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Jan Philipp Radtke
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Manuel Wiesenfarth
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Philipp Kickingereder
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Sebastian Bickelhaupt
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Tristan Anselm Kuder
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Albrecht Stenzinger
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Markus Hohenfellner
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Heinz-Peter Schlemmer
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - Klaus H Maier-Hein
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
| | - David Bonekamp
- From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.)
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Wiesenfarth M, Calderazzo S. Quantification of prior impact in terms of effective current sample size. Biometrics 2019; 76:326-336. [PMID: 31364156 DOI: 10.1111/biom.13124] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 07/25/2019] [Indexed: 02/06/2023]
Abstract
Bayesian methods allow borrowing of historical information through prior distributions. The concept of prior effective sample size (prior ESS) facilitates quantification and communication of such prior information by equating it to a sample size. Prior information can arise from historical observations; thus, the traditional approach identifies the ESS with such a historical sample size. However, this measure is independent of newly observed data, and thus would not capture an actual "loss of information" induced by the prior in case of prior-data conflict. We build on a recent work to relate prior impact to the number of (virtual) samples from the current data model and introduce the effective current sample size (ECSS) of a prior, tailored to the application in Bayesian clinical trial designs. Special emphasis is put on robust mixture, power, and commensurate priors. We apply the approach to an adaptive design in which the number of recruited patients is adjusted depending on the effective sample size at an interim analysis. We argue that the ECSS is the appropriate measure in this case, as the aim is to save current (as opposed to historical) patients from recruitment. Furthermore, the ECSS can help overcome lack of consensus in the ESS assessment of mixture priors and can, more broadly, provide further insights into the impact of priors. An R package accompanies the paper.
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Affiliation(s)
- Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Silvia Calderazzo
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
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Radtke JP, Giganti F, Wiesenfarth M, Stabile A, Marenco J, Orczyk C, Kasivisvanathan V, Nyarangi-Dix JN, Schütz V, Dieffenbacher S, Görtz M, Stenzinger A, Roth W, Freeman A, Punwani S, Bonekamp D, Schlemmer HP, Hohenfellner M, Emberton M, Moore CM. Prediction of significant prostate cancer in biopsy-naïve men: Validation of a novel risk model combining MRI and clinical parameters and comparison to an ERSPC risk calculator and PI-RADS. PLoS One 2019; 14:e0221350. [PMID: 31450235 PMCID: PMC6710031 DOI: 10.1371/journal.pone.0221350] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 08/05/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Risk models (RM) need external validation to assess their value beyond the setting in which they were developed. We validated a RM combining mpMRI and clinical parameters for the probability of harboring significant prostate cancer (sPC, Gleason Score ≥ 3+4) for biopsy-naïve men. MATERIAL AND METHODS The original RM was based on data of 670 biopsy-naïve men from Heidelberg University Hospital who underwent mpMRI with PI-RADS scoring prior to MRI/TRUS-fusion biopsy 2012-2015. Validity was tested by a consecutive cohort of biopsy-naïve men from Heidelberg (n = 160) and externally by a cohort of 133 men from University College London Hospital (UCLH). Assessment of validity was performed at fusion-biopsy by calibration plots, receiver operating characteristics curve and decision curve analyses. The RM`s performance was compared to ERSPC-RC3, ERSPC-RC3+PI-RADSv1.0 and PI-RADSv1.0 alone. RESULTS SPC was detected in 76 men (48%) at Heidelberg and 38 men (29%) at UCLH. The areas under the curve (AUC) were 0.86 for the RM in both cohorts. For ERSPC-RC3+PI-RADSv1.0 the AUC was 0.84 in Heidelberg and 0.82 at UCLH, for ERSPC-RC3 0.76 at Heidelberg and 0.77 at UCLH and for PI-RADSv1.0 0.79 in Heidelberg and 0.82 at UCLH. Calibration curves suggest that prevalence of sPC needs to be adjusted to local circumstances, as the RM overestimated the risk of harboring sPC in the UCLH cohort. After prevalence-adjustment with respect to the prevalence underlying ERSPC-RC3 to ensure a generalizable comparison, not only between the Heidelberg and die UCLH subgroup, the RM`s Net benefit was superior over the ERSPC`s and the mpMRI`s for threshold probabilities above 0.1 in both cohorts. CONCLUSIONS The RM discriminated well between men with and without sPC at initial MRI-targeted biopsy but overestimated the sPC-risk at UCLH. Taking prevalence into account, the model demonstrated benefit compared with clinical risk calculators and PI-RADSv1.0 in making the decision to biopsy men at suspicion of PC. However, prevalence differences must be taken into account when using or validating the presented risk model.
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Affiliation(s)
- Jan Philipp Radtke
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, United Kingdom
- Division of Surgery & Interventional Science, University College London, London, United Kingdom
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Armando Stabile
- Division of Surgery & Interventional Science, University College London, London, United Kingdom
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Jose Marenco
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Clement Orczyk
- Division of Surgery & Interventional Science, University College London, London, United Kingdom
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Veeru Kasivisvanathan
- Division of Surgery & Interventional Science, University College London, London, United Kingdom
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | | | - Viktoria Schütz
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany
| | - Svenja Dieffenbacher
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Magdalena Görtz
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany
| | | | - Wilfried Roth
- Institute of Pathology, University of Heidelberg, Heidelberg Germany
- Institute of Pathology, University Medicine Mainz, Mainz, Germany
| | - Alex Freeman
- Department of Pathology, University College Hospital, London, United Kingdom
| | - Shonit Punwani
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, United Kingdom
- Centre for Medical Imaging, University College London Hospitals NHS Foundation Trust, University College London, London, United Kingdom
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | | | | | - Mark Emberton
- Division of Surgery & Interventional Science, University College London, London, United Kingdom
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Caroline M. Moore
- Division of Surgery & Interventional Science, University College London, London, United Kingdom
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
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Nyarangi-Dix J, Wiesenfarth M, Bonekamp D, Hitthaler B, Schütz V, Dieffenbacher S, Mueller-Wolf M, Roth W, Stenzinger A, Duensing S, Roethke M, Teber D, Schlemmer HP, Hohenfellner M, Radtke JP. Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for the Prediction of Extraprostatic Disease-A Risk Model for Patient-tailored Risk Stratification When Planning Radical Prostatectomy. Eur Urol Focus 2018; 6:1205-1212. [PMID: 30477971 DOI: 10.1016/j.euf.2018.11.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [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: 09/15/2018] [Revised: 10/19/2018] [Accepted: 11/10/2018] [Indexed: 02/04/2023]
Abstract
BACKGROUND Multiparametric magnetic resonance imaging (mpMRI) facilitates the detection of significant prostate cancer. Therefore, addition of mpMRI to clinical parameters might improve the prediction of extraprostatic extension (EPE) in radical prostatectomy (RP) specimens. OBJECTIVE To investigate the accuracy of a novel risk model (RM) combining clinical and mpMRI parameters to predict EPE in RP specimens. DESIGN, SETTING, AND PARTICIPANTS We added prebiopsy mpMRI to clinical parameters and developed an RM to predict individual side-specific EPE (EPE-RM). Clinical parameters of 264 consecutive men with mpMRI prior to MRI/transrectal ultrasound fusion biopsy and subsequent RP between 2012 and 2015 were retrospectively analysed. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Multivariate regression analyses were used to determine significant EPE predictors for RM development. The prediction performance of the novel EPE-RM was compared with clinical T stage (cT), MR-European Society of Urogenital Radiology (ESUR) classification for EPE, two established nomograms (by Steuber et al and Ohori et al) and a clinical nomogram based on the coefficients of the established nomograms, and was constructed based on the data of the present cohort, using receiver operating characteristics (ROCs). For comparison, models' likelihood ratio (LR) tests and Vuong tests were used. Discrimination and calibration of the EPE-RM were validated based on resampling methods using bootstrapping. RESULTS AND LIMITATIONS International society of Urogenital Pathology grade on biopsy, ESUR criteria, prostate-specific antigen, cT, prostate volume, and capsule contact length were included in the EPE-RM. Calibration of the EPE-RM was good (error 0.018). The ROC area under the curve for the EPE-RM was larger (0.87) compared with cT (0.66), Memorial Sloan Kettering Cancer Center nomogram (0.73), Steuber nomogram (0.70), novel clinical nomogram (0.79), and ESUR classification (0.81). Based on LR and Vuong tests, the EPE-RM's model fit was significantly better than that of cT, all clinical models, and ESUR classification alone (p<0.001). Limitations include monocentric design and expert reading of MRI. CONCLUSIONS This novel EPE-RM, incorporating clinical and MRI parameters, performed better than contemporary clinical RMs and MRI predictors, therefore providing an accurate patient-tailored preoperative risk stratification of side-specific EPE. PATIENT SUMMARY Extraprostatic extension of prostate cancer can be predicted accurately using a combination of magnetic resonance imaging and clinical parameters. This novel risk model outperforms magnetic resonance imaging and clinical predictors alone and can be useful when planning nerve-sparing radical prostatectomy.
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Affiliation(s)
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Bertram Hitthaler
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany
| | - Viktoria Schütz
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany
| | - Svenja Dieffenbacher
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany; Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Maya Mueller-Wolf
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wilfried Roth
- Institute of Pathology Heidelberg, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology Heidelberg, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | - Stefan Duensing
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany
| | - Matthias Roethke
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dogu Teber
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany
| | | | | | - Jan Philipp Radtke
- Department of Urology, University Hospital Heidelberg, Heidelberg, Germany; Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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27
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Kesch C, Radtke JP, Wintsche A, Wiesenfarth M, Luttje M, Gasch C, Dieffenbacher S, Pecqueux C, Teber D, Hatiboglu G, Nyarangi-Dix J, Simpfendörfer T, Schönberg G, Dimitrakopoulou-Strauss A, Freitag M, Duensing A, Grüllich C, Jäger D, Götz M, Grabe N, Schweiger MR, Pahernik S, Perner S, Herpel E, Roth W, Wieczorek K, Maier-Hein K, Debus J, Haberkorn U, Giesel F, Galle J, Hadaschik B, Schlemmer HP, Hohenfellner M, Bonekamp D, Sültmann H, Duensing S. Correlation between genomic index lesions and mpMRI and 68Ga-PSMA-PET/CT imaging features in primary prostate cancer. Sci Rep 2018; 8:16708. [PMID: 30420756 PMCID: PMC6232089 DOI: 10.1038/s41598-018-35058-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 10/30/2018] [Indexed: 02/01/2023] Open
Abstract
Magnetic resonance imaging (MRI) and prostate specific membrane antigen (PSMA)- positron emission tomography (PET)/computed tomography (CT)-imaging of prostate cancer (PCa) are emerging techniques to assess the presence of significant disease and tumor progression. It is not known, however, whether and to what extent lesions detected by these imaging techniques correlate with genomic features of PCa. The aim of this study was therefore to define a genomic index lesion based on chromosomal copy number alterations (CNAs) as marker for tumor aggressiveness in prostate biopsies in direct correlation to multiparametric (mp) MRI and 68Ga-PSMA-PET/CT imaging features. CNA profiles of 46 biopsies from five consecutive patients with clinically high-risk PCa were obtained from radiologically suspicious and unsuspicious areas. All patients underwent mpMRI, MRI/TRUS-fusion biopsy, 68Ga-PSMA-PET/CT and a radical prostatectomy. CNAs were directly correlated to imaging features and radiogenomic analyses were performed. Highly significant CNAs (≥10 Mbp) were found in 22 of 46 biopsies. Chromosome 8p, 13q and 5q losses were the most common findings. There was an strong correspondence between the radiologic and the genomic index lesions. The radiogenomic analyses suggest the feasibility of developing radiologic signatures that can distinguish between genomically more or less aggressive lesions. In conclusion, imaging features of mpMRI and 68Ga-PSMA-PET/CT can guide to the genomically most aggressive lesion of a PCa. Radiogenomics may help to better differentiate between indolent and aggressive PCa in the future.
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Affiliation(s)
- Claudia Kesch
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Jan-Philipp Radtke
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Axel Wintsche
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107, Leipzig, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Mariska Luttje
- Imaging Division, University Medical Center Utrecht, Heidelberglaan 100, 3584, CX Utrecht, The Netherlands
| | - Claudia Gasch
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Svenja Dieffenbacher
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Carine Pecqueux
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Dogu Teber
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Gencay Hatiboglu
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Joanne Nyarangi-Dix
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Tobias Simpfendörfer
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Gita Schönberg
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - Antonia Dimitrakopoulou-Strauss
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Martin Freitag
- Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Anette Duensing
- Cancer Therapeutics Program and Department of Pathology, Hillman Cancer Center, University of Pittsburgh School of Medicine, 5117 Centre Avenue, Pittsburgh, PA, 15213, USA
| | - Carsten Grüllich
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Im Neuenheimer Feld 460, D-69120, Heidelberg, Germany
| | - Dirk Jäger
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Im Neuenheimer Feld 460, D-69120, Heidelberg, Germany
| | - Michael Götz
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Niels Grabe
- Hamamatsu Tissue Imaging and Analysis Center (TIGA), BIOQUANT, University of Heidelberg, Im Neuenheimer Feld 267, D-69120, Heidelberg, Germany
| | - Michal-Ruth Schweiger
- Functional Epigenomics, Center for Molecular Medicine Cologne (CMMC), University of Cologne, Robert-Koch-Strasse 21, D-50931, Cologne, Germany
| | - Sascha Pahernik
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany.,Department of Urology, University Hospital Nuremberg, Nuremberg, Germany
| | - Sven Perner
- Pathology of the University Hospital Schleswig-Holstein, Campus Lübeck and the Research Center Borstel, Leibniz Lung Center, Ratzeburger Allee 160, D-23538 Lübeck and Parkallee 1-40, D-23845, Borstel, Germany
| | - Esther Herpel
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, D-69120, Heidelberg, Germany
| | - Wilfried Roth
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, D-69120, Heidelberg, Germany.,Institute of Pathology, University Hospital Mainz, Mainz, Germany
| | - Kathrin Wieczorek
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, D-69120, Heidelberg, Germany.,Pathology Rosenheim, Rosenheim, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, University Hospital Heidelberg, Im Neuenheimer Feld 400, D-69120, Heidelberg, Germany
| | - Uwe Haberkorn
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany.,Department of Nuclear Medicine, University Hospital Heidelberg, Im Neuenheimer Feld 400, D-69120, Heidelberg, Germany
| | - Frederik Giesel
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany.,Department of Nuclear Medicine, University Hospital Heidelberg, Im Neuenheimer Feld 400, D-69120, Heidelberg, Germany
| | - Jörg Galle
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107, Leipzig, Germany
| | - Boris Hadaschik
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany.,Department of Urology, University Hospital Essen, Essen, Germany
| | - Heinz-Peter Schlemmer
- Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Markus Hohenfellner
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Holger Sültmann
- Cancer Genome Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Im Neuenheimer Feld 460, D-69120, Heidelberg, Germany.
| | - Stefan Duensing
- Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany. .,Molecular Urooncology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120, Heidelberg, Germany.
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28
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Bonekamp D, Kohl S, Wiesenfarth M, Schelb P, Radtke JP, Götz M, Kickingereder P, Yaqubi K, Hitthaler B, Gählert N, Kuder TA, Deister F, Freitag M, Hohenfellner M, Hadaschik BA, Schlemmer HP, Maier-Hein KH. Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values. Radiology 2018; 289:128-137. [DOI: 10.1148/radiol.2018173064] [Citation(s) in RCA: 120] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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29
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Kopp-Schneider A, Wiesenfarth M, Witt R, Edelmann D, Witt O, Abel U. Monitoring futility and efficacy in phase II trials with Bayesian posterior distributions-A calibration approach. Biom J 2018; 61:488-502. [PMID: 30175405 DOI: 10.1002/bimj.201700209] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [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: 10/25/2017] [Revised: 07/06/2018] [Accepted: 07/07/2018] [Indexed: 11/09/2022]
Abstract
A multistage single arm phase II trial with binary endpoint is considered. Bayesian posterior probabilities are used to monitor futility in interim analyses and efficacy in the final analysis. For a beta-binomial model, decision rules based on Bayesian posterior probabilities are converted to "traditional" decision rules in terms of number of responders among patients observed so far. Analytical derivations are given for the probability of stopping for futility and for the probability to declare efficacy. A workflow is presented on how to select the parameters specifying the Bayesian design, and the operating characteristics of the design are investigated. It is outlined how the presented approach can be transferred to statistical models other than the beta-binomial model.
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Affiliation(s)
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ruth Witt
- Clinical Trial Center, National Center for Tumor Diseases, Heidelberg, Germany.,Hopp Children's Cancer Center at NCT Heidelberg (KiTZ), Department of Pediatric Oncology and Hematology and Clinical Cooperation Unit Pediatric Oncology, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dominic Edelmann
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Olaf Witt
- Hopp Children's Cancer Center at NCT Heidelberg (KiTZ), Department of Pediatric Oncology and Hematology and Clinical Cooperation Unit Pediatric Oncology, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ulrich Abel
- Clinical Trial Center, National Center for Tumor Diseases, Heidelberg, Germany
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30
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Ross T, Zimmerer D, Vemuri A, Isensee F, Wiesenfarth M, Bodenstedt S, Both F, Kessler P, Wagner M, Müller B, Kenngott H, Speidel S, Kopp-Schneider A, Maier-Hein K, Maier-Hein L. Exploiting the potential of unlabeled endoscopic video data with self-supervised learning. Int J Comput Assist Radiol Surg 2018; 13:925-933. [PMID: 29704196 DOI: 10.1007/s11548-018-1772-0] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 04/16/2018] [Indexed: 11/27/2022]
Abstract
PURPOSE Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to the breakthrough successes of deep learning-based solutions for automatic image annotation, the availability of reference annotations for algorithm training is becoming a major bottleneck in the field. The purpose of this paper was to investigate the concept of self-supervised learning to address this issue. METHODS Our approach is guided by the hypothesis that unlabeled video data can be used to learn a representation of the target domain that boosts the performance of state-of-the-art machine learning algorithms when used for pre-training. Core of the method is an auxiliary task based on raw endoscopic video data of the target domain that is used to initialize the convolutional neural network (CNN) for the target task. In this paper, we propose the re-colorization of medical images with a conditional generative adversarial network (cGAN)-based architecture as auxiliary task. A variant of the method involves a second pre-training step based on labeled data for the target task from a related domain. We validate both variants using medical instrument segmentation as target task. RESULTS The proposed approach can be used to radically reduce the manual annotation effort involved in training CNNs. Compared to the baseline approach of generating annotated data from scratch, our method decreases exploratively the number of labeled images by up to 75% without sacrificing performance. Our method also outperforms alternative methods for CNN pre-training, such as pre-training on publicly available non-medical (COCO) or medical data (MICCAI EndoVis2017 challenge) using the target task (in this instance: segmentation). CONCLUSION As it makes efficient use of available (non-)public and (un-)labeled data, the approach has the potential to become a valuable tool for CNN (pre-)training.
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Affiliation(s)
- Tobias Ross
- Computer Assisted Medical Interventions, German Cancer Research Center, Im Neuenheimer Feld 581, 69210, Heidelberg, Germany.
| | - David Zimmerer
- Medical Image Computing, German Cancer Research Center, Im Neuenheimer Feld 581, 69210, Heidelberg, Germany
| | - Anant Vemuri
- Computer Assisted Medical Interventions, German Cancer Research Center, Im Neuenheimer Feld 581, 69210, Heidelberg, Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center, Im Neuenheimer Feld 581, 69210, Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, 69210, Heidelberg, Germany
| | - Sebastian Bodenstedt
- Translational Surgical Oncology, National Center for Tumor Diseases (NCT), Fetscherstrasse 74, 01307, Dresden, Germany
| | - Fabian Both
- understand.ai, Hirschstr. 71, 76133, Karlsruhe, Germany
| | | | - Martin Wagner
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69210, Heidelberg, Germany
| | - Beat Müller
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69210, Heidelberg, Germany
| | - Hannes Kenngott
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69210, Heidelberg, Germany
| | - Stefanie Speidel
- Translational Surgical Oncology, National Center for Tumor Diseases (NCT), Fetscherstrasse 74, 01307, Dresden, Germany
| | - Annette Kopp-Schneider
- Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, 69210, Heidelberg, Germany
| | - Klaus Maier-Hein
- Medical Image Computing, German Cancer Research Center, Im Neuenheimer Feld 581, 69210, Heidelberg, Germany
| | - Lena Maier-Hein
- Computer Assisted Medical Interventions, German Cancer Research Center, Im Neuenheimer Feld 581, 69210, Heidelberg, Germany
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31
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Nyarangi-Dix J, Wörner J, Kopp-Schneider A, Schütz V, Bonekamp D, Wiesenfarth M, Stenzinger A, Roth W, Hadaschik B, Schlemmer HP, Hatiboglu G, Teber D, Hohenfellner M, Radtke JP. MP20-07 PREOPERATIVE MRI AND CLINICAL PARAMETERS FOR ACCURATE PREDICTION OF CONTINENCE RECOVERY FOLLOWING RADICAL PROSTATECTOMY – MEMBRANOUS URETHRAL LENGTH ON MRI OUTPERFORMS PREDICTIVE MODELS. J Urol 2018. [DOI: 10.1016/j.juro.2018.02.677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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32
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Radtke JP, Giganti F, Wiesenfarth M, Marenco J, Orczyk C, Kasivisvanathan V, Stabile A, Kesch C, Nyarangi-Dix J, Schütz V, Stenzinger A, Roth W, Teber D, Bonekamp D, Schlemmer HP, Hohenfellner M, Emberton M, Hadaschik B, Moore C. MP77-20 PREDICTION OF SIGNIFICANT PROSTATE CANCER IN BIOPSY-NAÏVE MEN: EXTERNAL VALIDATION OF A NOVEL RISK MODEL COMBINING MRI AND CLINICAL PARAMETERS. J Urol 2018. [DOI: 10.1016/j.juro.2018.02.2608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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33
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Kesch C, Radtke JP, Wintsche A, Wiesenfarth M, Luttje M, Gasch C, Dieffenbacher S, Teber D, Hatiboglu G, Nyarangi-Dix J, Simpfendörfer T, Dimitrakopoulou-Strauß A, Freitag M, Duensing A, Grüllich C, Götz M, Jäger D, Grabe N, Schweiger MR, Giesel FL, Roth W, Perner S, Galle J, Maier-Hein K, Hadaschik BA, Schlemmer HP, Hohenellner M, Bonekamp D, Sültmann H, Duensing S. PD47-04 CORRELATION BETWEEN GENOMIC INDEX LESIONS, MULTI-PARAMETRIC MRI AND 68GA-PSMA-PET/CT IMAGING FEATURES IN PRIMARY PROSTATE CANCER. J Urol 2018. [DOI: 10.1016/j.juro.2018.02.2164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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34
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Lipka DB, Witte T, Toth R, Yang J, Wiesenfarth M, Nöllke P, Fischer A, Brocks D, Gu Z, Park J, Strahm B, Wlodarski M, Yoshimi A, Claus R, Lübbert M, Busch H, Boerries M, Hartmann M, Schönung M, Kilik U, Langstein J, Wierzbinska JA, Pabst C, Garg S, Catalá A, De Moerloose B, Dworzak M, Hasle H, Locatelli F, Masetti R, Schmugge M, Smith O, Stary J, Ussowicz M, van den Heuvel-Eibrink MM, Assenov Y, Schlesner M, Niemeyer C, Flotho C, Plass C. RAS-pathway mutation patterns define epigenetic subclasses in juvenile myelomonocytic leukemia. Nat Commun 2017; 8:2126. [PMID: 29259247 PMCID: PMC5736667 DOI: 10.1038/s41467-017-02177-w] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 11/13/2017] [Indexed: 01/15/2023] Open
Abstract
Juvenile myelomonocytic leukemia (JMML) is an aggressive myeloproliferative disorder of early childhood characterized by mutations activating RAS signaling. Established clinical and genetic markers fail to fully recapitulate the clinical and biological heterogeneity of this disease. Here we report DNA methylome analysis and mutation profiling of 167 JMML samples. We identify three JMML subgroups with unique molecular and clinical characteristics. The high methylation group (HM) is characterized by somatic PTPN11 mutations and poor clinical outcome. The low methylation group is enriched for somatic NRAS and CBL mutations, as well as for Noonan patients, and has a good prognosis. The intermediate methylation group (IM) shows enrichment for monosomy 7 and somatic KRAS mutations. Hypermethylation is associated with repressed chromatin, genes regulated by RAS signaling, frequent co-occurrence of RAS pathway mutations and upregulation of DNMT1 and DNMT3B, suggesting a link between activation of the DNA methylation machinery and mutational patterns in JMML.
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MESH Headings
- Antineoplastic Agents/therapeutic use
- Biopsy
- Child
- Child, Preschool
- Chromatin/genetics
- Chromatin/metabolism
- DNA (Cytosine-5-)-Methyltransferase 1/metabolism
- DNA (Cytosine-5-)-Methyltransferases/metabolism
- DNA Methylation
- DNA Mutational Analysis
- Epigenomics
- Female
- Gene Expression Regulation, Leukemic
- Hematopoietic Stem Cell Transplantation
- Humans
- Infant
- Leukemia, Myelomonocytic, Juvenile/genetics
- Leukemia, Myelomonocytic, Juvenile/mortality
- Leukemia, Myelomonocytic, Juvenile/pathology
- Leukemia, Myelomonocytic, Juvenile/therapy
- Male
- Mutation
- Noonan Syndrome/genetics
- Noonan Syndrome/pathology
- Prognosis
- Prospective Studies
- Protein Tyrosine Phosphatase, Non-Receptor Type 11/genetics
- Protein Tyrosine Phosphatase, Non-Receptor Type 11/metabolism
- Proto-Oncogene Proteins c-cbl
- Proto-Oncogene Proteins p21(ras)/genetics
- Proto-Oncogene Proteins p21(ras)/metabolism
- Signal Transduction/genetics
- Up-Regulation
- DNA Methyltransferase 3B
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Affiliation(s)
- Daniel B Lipka
- Regulation of Cellular Differentiation Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany.
- Department of Hematology and Oncology, Medical Center, Otto-von-Guericke-University, Leipziger Strasse 44, 39120, Magdeburg, Germany.
- Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke-University, Leipziger Strasse 44, 39120, Magdeburg, Germany.
| | - Tania Witte
- Regulation of Cellular Differentiation Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
- Cancer Epigenetics Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Reka Toth
- Computational Epigenomics Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Jing Yang
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Peter Nöllke
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine Medical Center, Faculty of Medicine, University of Freiburg, Heiliggeiststrasse 1, 79106, Freiburg, Germany
| | - Alexandra Fischer
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine Medical Center, Faculty of Medicine, University of Freiburg, Heiliggeiststrasse 1, 79106, Freiburg, Germany
| | - David Brocks
- Cancer Epigenetics Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Zuguang Gu
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Jeongbin Park
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Brigitte Strahm
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine Medical Center, Faculty of Medicine, University of Freiburg, Heiliggeiststrasse 1, 79106, Freiburg, Germany
| | - Marcin Wlodarski
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine Medical Center, Faculty of Medicine, University of Freiburg, Heiliggeiststrasse 1, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK), 79106, Freiburg, Germany
| | - Ayami Yoshimi
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine Medical Center, Faculty of Medicine, University of Freiburg, Heiliggeiststrasse 1, 79106, Freiburg, Germany
| | - Rainer Claus
- Division of Hematology, Oncology and Stem Cell Transplantation, University Medical Center, Hugstetter Strasse 55, 79106, Freiburg, Germany
| | - Michael Lübbert
- Division of Hematology, Oncology and Stem Cell Transplantation, University Medical Center, Hugstetter Strasse 55, 79106, Freiburg, Germany
| | - Hauke Busch
- Institute of Molecular Medicine and Cell Research, University of Freiburg, Stefan-Meier-Strasse 17, 79104, Freiburg, Germany
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Melanie Boerries
- Institute of Molecular Medicine and Cell Research, University of Freiburg, Stefan-Meier-Strasse 17, 79104, Freiburg, Germany
- German Cancer Consortium (DKTK), 79106, Freiburg, Germany
- German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Mark Hartmann
- Regulation of Cellular Differentiation Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Maximilian Schönung
- Regulation of Cellular Differentiation Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Umut Kilik
- Regulation of Cellular Differentiation Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Jens Langstein
- Regulation of Cellular Differentiation Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Justyna A Wierzbinska
- Regulation of Cellular Differentiation Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
- Cancer Epigenetics Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Caroline Pabst
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, INF 410, 69120, Heidelberg, Germany
| | - Swati Garg
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, INF 410, 69120, Heidelberg, Germany
| | - Albert Catalá
- Department of Hematology and Oncology, Hospital Sant Joan de Déu, Passeig de Sant Joan de Déu, 2, 08950, Esplugues de Llobrega, Barcelona, Spain
| | - Barbara De Moerloose
- Department of Pediatric Hematology-Oncology and Stem Cell Transplantation, Ghent University Hospital, De Pintelaan 185, 9000, Ghent, Belgium
| | - Michael Dworzak
- St. Anna Children's Hospital and Children's Cancer Research Institute, Medical University of Vienna, Zimmermannplatz 10, 1090, Vienna, Austria
| | - Henrik Hasle
- Department of Pediatrics, Aarhus University Hospital Skejby, Palle Juul-Jensens Boulevard 82, 8200, Aarhus, Denmark
| | - Franco Locatelli
- Department of Pediatric Hematology and Oncology, Bambino Gesú Children's Hospital, University of Pavia, Piazza S. Onofrio 4, Rome, 00165, Italy
| | - Riccardo Masetti
- Department of Pediatric Oncology and Hematology, University of Bologna, Via Massarenti 11, 40138, Bologna, Italy
| | - Markus Schmugge
- Department of Hematology and Oncology, University Children's Hospital, Steinwiesstrasse 75, 8032, Zurich, Switzerland
| | - Owen Smith
- Department of Paediatric Oncology and Haematology, Our Lady's Children's Hospital Crumlin, Dublin, 12, Ireland
| | - Jan Stary
- Department of Pediatric Hematology and Oncology, Charles University and University Hospital Motol, V Úvalu 84, 150 06, Prague 5, Czech Republic
| | - Marek Ussowicz
- Department of Pediatric Hematology, Oncology and BMT, Wroclaw Medical University, ul. Borowska 213, 50-556, Wroclaw, Poland
| | | | - Yassen Assenov
- Computational Epigenomics Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
| | - Matthias Schlesner
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany
- Bioinformatics and Omics Data Analytics (B240), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Charlotte Niemeyer
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine Medical Center, Faculty of Medicine, University of Freiburg, Heiliggeiststrasse 1, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK), 79106, Freiburg, Germany
| | - Christian Flotho
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine Medical Center, Faculty of Medicine, University of Freiburg, Heiliggeiststrasse 1, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK), 79106, Freiburg, Germany
| | - Christoph Plass
- Cancer Epigenetics Group, Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, 69120, Heidelberg, Germany.
- German Cancer Consortium (DKTK), 69120, Heidelberg, Germany.
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Schroeder L, Wichmann G, Willner M, Michel A, Wiesenfarth M, Flechtenmacher C, Gradistanac T, Pawlita M, Dietz A, Waterboer T, Holzinger D. Antibodies against human papillomaviruses as diagnostic and prognostic biomarker in patients with neck squamous cell carcinoma from unknown primary tumor. Int J Cancer 2017; 142:1361-1368. [PMID: 29159804 DOI: 10.1002/ijc.31167] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [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: 07/04/2017] [Revised: 10/30/2017] [Accepted: 11/06/2017] [Indexed: 12/19/2022]
Abstract
Treatment of patients with neck lymph node metastasis of squamous cell carcinoma (SCC) from unknown primary tumor (NSCCUP) is challenging due to the risk of missing occult tumors or inducing toxicity to unaffected sites. Human papillomavirus (HPV) is a promising biomarker given its causal link to oropharyngeal SCC and superior survival of patients with HPV-driven oropharyngeal SCC and NSCCUP. Identification of HPV-driven NSCCUP could focus diagnostic work-up and treatment on the oropharynx. For the first time, we assessed HPV antibodies and their prognostic value in NSCCUP patients. Antibodies against E6 and E7 (HPV16/18/31/33/35), E1 and E2 (HPV16/18) were assessed in 46 NSCCUP patients in sera collected at diagnosis, and in follow-up sera from five patients. In 28 patients, HPV tumor status was determined using molecular markers (HPV DNA, mRNA and cellular p16INK4a ). Thirteen (28%) NSCCUP patients were HPV-seropositive for HPV16, 18, 31, or 33. Of eleven patients with HPV-driven NSCCUP, ten were HPV-seropositive, while all 17 patients with non-HPV-driven NSCCUP were HPV-seronegative, resulting in 91% sensitivity (95% CI: 59-100%) and 100% specificity (95% CI: 80-100%). HPV antibody levels decreased after curative treatment. Recurrence was associated with increasing levels in an individual case. HPV-seropositive patients had a better overall and progression-free survival with hazard ratios of 0.09 (95% CI: 0.01-0.42) and 0.03 (95% CI: 0.002-0.18), respectively. For the first time, seropositivity to HPV proteins is described in NSCCUP patients, and high sensitivity and specificity for HPV-driven NSCCUP are demonstrated. HPV seropositivity appears to be a reliable diagnostic and prognostic biomarker for patients with HPV-driven NSCCUP.
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Affiliation(s)
- Lea Schroeder
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer Program, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Gunnar Wichmann
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Maria Willner
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Angelika Michel
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer Program, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christa Flechtenmacher
- NCT Tissue Bank of the National Center of Tumor Diseases (NCT) Heidelberg and Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Tanja Gradistanac
- Department of Pathology, University Hospital Leipzig, Leipzig, Germany
| | - Michael Pawlita
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer Program, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andreas Dietz
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Tim Waterboer
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer Program, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dana Holzinger
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer Program, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Frölich M, Huber M, Wiesenfarth M. The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation. Comput Stat Data Anal 2017. [DOI: 10.1016/j.csda.2017.05.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Holzinger D, Wichmann G, Baboci L, Michel A, Höfler D, Wiesenfarth M, Schroeder L, Boscolo‐Rizzo P, Herold‐Mende C, Dyckhoff G, Boehm A, Del Mistro A, Bosch FX, Dietz A, Pawlita M, Waterboer T. Sensitivity and specificity of antibodies against HPV16 E6 and other early proteins for the detection of HPV16‐driven oropharyngeal squamous cell carcinoma. Int J Cancer 2017; 140:2748-2757. [DOI: 10.1002/ijc.30697] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Revised: 02/21/2017] [Accepted: 02/27/2017] [Indexed: 01/12/2023]
Affiliation(s)
- Dana Holzinger
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer ProgramGerman Cancer Research Center (DKFZ)Heidelberg Germany
| | - Gunnar Wichmann
- Department of OtorhinolaryngologyUniversity Hospital LeipzigLeipzig Germany
| | - Lorena Baboci
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer ProgramGerman Cancer Research Center (DKFZ)Heidelberg Germany
- Department of Oncology and Surgical SciencesUniversity of PaduaPadua Italy
| | - Angelika Michel
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer ProgramGerman Cancer Research Center (DKFZ)Heidelberg Germany
| | - Daniela Höfler
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer ProgramGerman Cancer Research Center (DKFZ)Heidelberg Germany
| | - Manuel Wiesenfarth
- Division of BiostatisticsGerman Cancer Research CenterHeidelberg Germany
| | - Lea Schroeder
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer ProgramGerman Cancer Research Center (DKFZ)Heidelberg Germany
| | - Paolo Boscolo‐Rizzo
- Department of NeurosciencesENT Clinic and Regional Center for Head and Neck Cancer, University of PaduaTreviso Italy
| | - Christel Herold‐Mende
- Department of Otorhinolaryngology, Head and Neck SurgeryHeidelberg UniversityHeidelberg Germany
- Department of Neurosurgery, Division of Experimental NeurosurgeryHeidelberg UniversityHeidelberg Germany
| | - Gerhard Dyckhoff
- Department of Otorhinolaryngology, Head and Neck SurgeryHeidelberg UniversityHeidelberg Germany
| | - Andreas Boehm
- Department of OtorhinolaryngologyUniversity Hospital LeipzigLeipzig Germany
| | - Annarosa Del Mistro
- Department of Immunology and Molecular OncologyIRCCS Veneto Institute of Oncology (IOV)Padua Italy
| | - Franz X. Bosch
- Department of Otorhinolaryngology, Head and Neck SurgeryHeidelberg UniversityHeidelberg Germany
| | - Andreas Dietz
- Department of OtorhinolaryngologyUniversity Hospital LeipzigLeipzig Germany
| | - Michael Pawlita
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer ProgramGerman Cancer Research Center (DKFZ)Heidelberg Germany
| | - Tim Waterboer
- Division of Molecular Diagnostics of Oncogenic Infections, Infection, Inflammation and Cancer ProgramGerman Cancer Research Center (DKFZ)Heidelberg Germany
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Bonekamp D, Bonekamp D, Hadaschik B, Wiesenfarth M, Wieczorek K, Götz M, Schlemmer H, Maier-Hein K. Vergleich von Radiomics, quantitativen ADC Messungen und PI-RADS zur Detektion des signifikanten Prostatakarzinoms. ROFO-FORTSCHR RONTG 2017. [DOI: 10.1055/s-0037-1600459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- D Bonekamp
- Deutsches Krebsforschungszentrum, Radiologie, Heidelberg
| | - D Bonekamp
- Deutsches Krebsforschungszentrum, Radiologie, Heidelberg
| | - B Hadaschik
- Universitätsklinikum Heidelberg, Urologie, Heidelberg
| | - M Wiesenfarth
- Deutsches Krebsforschungszentrum, Biostatistik, Heidelberg
| | - K Wieczorek
- Universitätsklinikum Heidelberg, Pathologie, Heidelberg
| | - M Götz
- Deutsches Krebsforschungszentrum, Medizinische Informatik, Heidelberg
| | - H Schlemmer
- Deutsches Krebsforschungszentrum, Radiologie, Heidelberg
| | - K Maier-Hein
- Deutsches Krebsforschungszentrum, Medizinische Informatik, Heidelberg
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Schroeder L, Boscolo-Rizzo P, Dal Cin E, Romeo S, Baboci L, Dyckhoff G, Hess J, Lucena-Porcel C, Byl A, Becker N, Alemany L, Castellsagué X, Quer M, León X, Wiesenfarth M, Pawlita M, Holzinger D. Human papillomavirus as prognostic marker with rising prevalence in neck squamous cell carcinoma of unknown primary: A retrospective multicentre study. Eur J Cancer 2017; 74:73-81. [DOI: 10.1016/j.ejca.2016.12.020] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 10/28/2016] [Accepted: 12/18/2016] [Indexed: 10/20/2022]
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Bickelhaupt S, Paech D, Kickingereder P, Steudle F, Lederer W, Daniel H, Götz M, Gählert N, Tichy D, Wiesenfarth M, Laun FB, Maier-Hein KH, Schlemmer HP, Bonekamp D. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging 2017; 46:604-616. [PMID: 28152264 DOI: 10.1002/jmri.25606] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [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: 10/12/2016] [Accepted: 12/07/2016] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T2 -weighted sequences. MATERIALS AND METHODS From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T2 -weighted, (T2 w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted. From ueMRI-derived radiomic features, three Lasso-supervised machine-learning classifiers were constructed and compared with the clinical performance of a highly experienced radiologist: 1) univariate mean ADC model, 2) unconstrained radiomic model, 3) constrained radiomic model with mandatory inclusion of mean ADC. RESULTS The unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2%/85.1%, compared to 77.4% for mean ADC and 95.9%/95.9% for the experienced radiologist using ceMRI/ueMRI. CONCLUSION In this pilot study we identified two ueMRI radiomics classifiers that performed well in the differentiation of malignant from benign lesions and achieved higher performance than the mean ADC parameter alone. Classification was lower than the almost perfect performance of a highly experienced breast radiologist. The potential of radiomics to provide a training-independent diagnostic decision tool is indicated. A performance reaching the human expert would be highly desirable and based on our results is considered possible when the concept is extended in larger cohorts with further development and validation of the technique. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:604-616.
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Affiliation(s)
| | - Daniel Paech
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Franziska Steudle
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Lederer
- Radiological Clinic at the ATOS Clinic Heidelberg, Heidelberg, Germany
| | - Heidi Daniel
- Radiology Center Mannheim (RZM), Mannheim, Germany
| | - Michael Götz
- Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nils Gählert
- Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Diana Tichy
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik B Laun
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus H Maier-Hein
- Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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41
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Henrich KO, Bender S, Saadati M, Dreidax D, Gartlgruber M, Shao C, Herrmann C, Wiesenfarth M, Parzonka M, Wehrmann L, Fischer M, Duffy DJ, Bell E, Torkov A, Schmezer P, Plass C, Höfer T, Benner A, Pfister SM, Westermann F. Integrative Genome-Scale Analysis Identifies Epigenetic Mechanisms of Transcriptional Deregulation in Unfavorable Neuroblastomas. Cancer Res 2016; 76:5523-37. [PMID: 27635046 DOI: 10.1158/0008-5472.can-15-2507] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 05/29/2016] [Indexed: 11/16/2022]
Abstract
The broad clinical spectrum of neuroblastoma ranges from spontaneous regression to rapid progression despite intensive multimodal therapy. This diversity is not fully explained by known genetic aberrations, suggesting the possibility of epigenetic involvement in pathogenesis. In pursuit of this hypothesis, we took an integrative approach to analyze the methylomes, transcriptomes, and copy number variations in 105 cases of neuroblastoma, complemented by primary tumor- and cell line-derived global histone modification analyses and epigenetic drug treatment in vitro We found that DNA methylation patterns identify divergent patient subgroups with respect to survival and clinicobiologic variables, including amplified MYCN Transcriptome integration and histone modification-based definition of enhancer elements revealed intragenic enhancer methylation as a mechanism for high-risk-associated transcriptional deregulation. Furthermore, in high-risk neuroblastomas, we obtained evidence for cooperation between PRC2 activity and DNA methylation in blocking tumor-suppressive differentiation programs. Notably, these programs could be re-activated by combination treatments, which targeted both PRC2 and DNA methylation. Overall, our results illuminate how epigenetic deregulation contributes to neuroblastoma pathogenesis, with novel implications for its diagnosis and therapy. Cancer Res; 76(18); 5523-37. ©2016 AACR.
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Affiliation(s)
- Kai-Oliver Henrich
- Neuroblastoma Genomics B087, German Cancer Research Center, Heidelberg, Germany. k.henrich@dkfz
| | - Sebastian Bender
- Division of Pediatric Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center, Heidelberg, Germany & Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Germany
| | - Maral Saadati
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Daniel Dreidax
- Neuroblastoma Genomics B087, German Cancer Research Center, Heidelberg, Germany
| | - Moritz Gartlgruber
- Neuroblastoma Genomics B087, German Cancer Research Center, Heidelberg, Germany
| | - Chunxuan Shao
- Division of Theoretical Systems Biology, German Cancer Research Center, Heidelberg, Germany
| | - Carl Herrmann
- Division of Theoretical Bioinformatics, German Cancer Research Center, Institute of Pharmacy and Molecular Biotechnology, Bioquant, University of Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Martha Parzonka
- Neuroblastoma Genomics B087, German Cancer Research Center, Heidelberg, Germany
| | - Lea Wehrmann
- Neuroblastoma Genomics B087, German Cancer Research Center, Heidelberg, Germany
| | - Matthias Fischer
- Department of Pediatric Oncology, University Children's Hospital, and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - David J Duffy
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
| | - Emma Bell
- Department of Pathology, University of Cambridge, Cambridge, United Kingdom
| | - Alica Torkov
- Neuroblastoma Genomics B087, German Cancer Research Center, Heidelberg, Germany
| | - Peter Schmezer
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany
| | - Christoph Plass
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center, Heidelberg, Germany
| | - Axel Benner
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Stefan M Pfister
- Division of Pediatric Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center, Heidelberg, Germany & Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Germany
| | - Frank Westermann
- Neuroblastoma Genomics B087, German Cancer Research Center, Heidelberg, Germany. k.henrich@dkfz
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Wiesenfarth M. Bayesian Nonparametric Data Analysis. P. Müller, F. A. Quintana, A. Jara, and T. Hanson (2015). Springer Series in Statistics. New York, NY: Springer. 193 pages, ISBN: 978-3-319-18967-3. Biom J 2016. [DOI: 10.1002/bimj.201600006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Manuel Wiesenfarth
- Division of Biostatistics; German Cancer Research Center; Heidelberg Germany
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Lipka DB, Witte T, Wiesenfarth M, Zucknick M, Konermann C, Gu L, Claus R, Nöllke P, Niemeyer C, Flotho C, Plass C. Genome-wide DNA methylation profiling reveals two prognostically distinct subgroups of juvenile myelomonocytic leukemia. Exp Hematol 2015. [DOI: 10.1016/j.exphem.2015.06.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wiesenfarth M, Krivobokova T, Klasen S, Sperlich S. Direct Simultaneous Inference in Additive Models and Its Application to Model Undernutrition. J Am Stat Assoc 2012. [DOI: 10.1080/01621459.2012.682809] [Citation(s) in RCA: 9] [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] [Indexed: 10/27/2022]
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