+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

          Related collections

          Most cited references 43

          • Record: found
          • Abstract: found
          • Article: not found

          Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.

          The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer's histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma's aggressiveness and a patient's prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P ≤ 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast cancer.
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification

              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Predicting cancer outcomes from histology and genomics using convolutional networks

              Significance Predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers. This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. Our approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes.

                Author and article information

                J Pathol
                J. Pathol
                The Journal of Pathology
                John Wiley & Sons, Ltd (Chichester, UK )
                03 September 2019
                November 2019
                : 249
                : 3 ( doiID: 10.1002/path.v249.3 )
                : 286-294
                [ 1 ] Regulatory and Clinical Affairs PathAI Boston MA USA
                [ 2 ] Department of Pathology University of Pittsburgh Medical Center Pittsburgh PA USA
                [ 3 ] Amgen Research, Comparative Biology and Safety Sciences Amgen Inc. South San Francisco CA USA
                [ 4 ] Department of Pathology and Laboratory Medicine Drexel University College of Medicine Philadelphia PA USA
                [ 5 ] Department of Pathology Radboud University Medical Center Nijmegen The Netherlands
                [ 6 ] Center for Medical Image Science and Visualization Linköping University Linköping Sweden
                [ 7 ] Department of Pathology Moffitt Cancer Center Tampa FL USA
                [ 8 ] Data Science Department Chan Zuckerberg Biohub San Francisco CA USA
                [ 9 ] Department of Pathology The Ohio State University Columbus OH USA
                [ 10 ] Hyman, Phelps & McNamara, P.C Washington DC USA
                [ 11 ] PathAI Boston MA USA
                [ 12 ] Department of Development Sciences Genentech Inc. South San Francisco CA USA
                Author notes
                [* ]Correspondence to: C Kozlowski, Genentech Inc, 1 DNA Way, South San Francisco, CA 94080, USA. E‐mail: cleopatk@
                © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

                This is an open access article under the terms of the License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 0, Tables: 1, Pages: 9, Words: 7700
                Custom metadata
                November 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.1 mode:remove_FC converted:13.11.2019


                Comment on this article