Computational pathology and the understanding of disease

The Journal of PathologyVolume 249, Issue 2 p. 141-142 EditorialFree Access Computational pathology and the understanding of disease First published: 12 August 2019 https://doi.org/10.1002/path.5337AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Computational pathology Computational pathology encompasses a range of approaches, all of which share the application of computational analysis to the study and diagnosis of disease. With increasing generation of whole slide images from tissue sections mounted on glass slides, there is a rapidly increasing resource of images derived from tissues that can be used for a wide range of analyses. These range from direct viewing for diagnostic purposes, with the consequent change from image viewing using a light microscope to a computer screen 1, to advanced computational investigation of these digitized images, employing artificial intelligence to derive increasingly complex information from tissue images. This is addressed in a recent paper in the Journal by Colling et al, who set out a roadmap for the development of software tools for approved clinical use 2. The hardware required to generate whole slide images is increasingly available and the use of whole slide images for routine diagnostic pathology is becoming increasingly common. This ready availability of whole slide images raises many issues, from the ethics of using such images for research, through the use of digital images to add quantitation to diagnostic assessment, to the stratification of images based solely on computational assessment. In another recent paper in the Journal, the current position of computational pathology is reviewed and summarized by Abels et al 3. In addition to providing a very useful glossary of terms, they discuss the various issues facing the implementation of computational pathology approaches in diagnostic and research practice. This has wide applicability and clarifies the notion that computational pathology will affect everyone involved in tissue-based diagnostics and/or research. This includes the whole spectrum of tissue analysis, from regular diagnosis based on H&E-stained sections 4, 5 and immunohistochemistry 6, through individual research projects 7 to clinical trials 8, where pathology review, for example, is increasingly performed on whole slide images. As the technology is adopted, it is crucial that standards are defined and implemented to ensure that the quality of diagnosis and research based on whole slide images is ‘noninferior’ to that using glass slides 9, 10. Moreover, the introduction of ancillary analyses, for example assessment of immunostaining 6 or measurement of tissue structures, requires rigorous quality control to ensure that clinical application is evidence-based. This is particularly important as data accrue to show that computational analysis can, for example, identify molecular abnormalities such as microsatellite instability directly from tissue morphology 11. The availability of annotated digital image resources such as those provided by The Cancer Genome Atlas, which are accompanied by clinicopathological and genomic/epigenomic/proteomic data 12, will help clinical and research communities to identify, test and verify algorithms. Call for papers Given the rapidly increasing importance of computational pathology to the study and diagnosis of disease, The Journal of Pathology invites submissions of primary research that uses computational methods to investigate disease processes. We are interested in receiving submissions that address any aspect of computational pathology in this context. Examples include, but are not restricted to: Novel computational approaches to the investigation of disease in tissues. Application of computational techniques to understanding disease mechanisms. Validation of computational approaches for the identification of molecular abnormalities in tissues. Manuscripts should ideally offer clinically relevant findings and/or mechanistic insights. Timely reviews and perspectives will also be welcomed. Correctly formatted manuscripts will be fast-tracked and expertly peer-reviewed, aiming for a final decision within 10 working days. We would anticipate that initial online publication would occur within 7 days of acceptance. Once published, each article will be made free to view online, facilitating dissemination within the community. In your covering letter, please mention that the submission is a response to the ‘Computational Pathology Call’. Please note that the manuscript must contain full details of the hardware and software used in the analyses either in the main manuscript or as supplementary material. We look forward to receiving your manuscript via https://mc.manuscriptcentral.com/jpath. References 1Snead DR, Tsang YW, Meskiri A, et al. Validation of digital pathology imaging for primary histopathological diagnosis. Histopathology 2016; 68: 1063– 1072. 2Colling R, Pitman H, Oien K, et al. Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J Pathol 2019; 249: 143– 150. 3Abels E, Pantanowitz L, Aeffner F, et al. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol 2019. https://doi.org/10.1002/path.5331. 4Treanor D, Gallas BD, Gavrielides MA, et al. Evaluating whole slide imaging: a working group opportunity. J Pathol Inform 2015; 6: 4. 5Liu Y, Kohlberger T, Norouzi M, et al. Artificial intelligence-based breast cancer nodal metastasis detection: insights into the black box for pathologist. Arch Pathol Lab Med 2019; 143: 859– 868. 6Qaiser T, Mukherjee A, Reddy Pb C, et al. HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology 2018; 72: 227– 238. 7Kather JN, Suarez-Carmona M, Charoentong P, et al. Topography of cancer-associated immune cells in human solid tumors. eLife 2018; 7: e36967. 8Pell R, Oien K, Robinson M, et al. The use of digital pathology and image analysis in clinical trials. J Pathol Clin Res 2019; 5: 81– 90. 9Williams BJ, Bottoms D, Treanor D. Future-proofing pathology: the case for clinical adoption of digital pathology. J Clin Pathol 2017; 70: 1010– 1018. 10Williams BJ, Knowles C, Treanor D. Maintaining quality diagnosis with digital pathology: a practical guide to ISO 15189 accreditation. J Clin Pathol 2019. https://doi.org/10.1136/jclinpath-2019-205944. 11Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med 2019; 25: 1054– 1056. 12Cooper LA, Demicco EG, Saltz JH, et al. PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective. J Pathol 2018; 244: 512– 524. Volume249, Issue2October 2019Pages 141-142 ReferencesRelatedInformation