Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images

Siegel, 2021, Cancer Statistics, 2021, CA Cancer J Clin, 71, 7, 10.3322/caac.21654 WHO Classification of Tumours Editorial Board. Female Genital Tumours. WHO Classification of Tumours (IARC Lyon, France, 2020). Köbel, 2008, Ovarian carcinoma subtypes are different diseases: implications for biomarker studies, PLoS Med, 5, 1749, 10.1371/journal.pmed.0050232 Gilks, 2013, Poor interobserver reproducibility in the diagnosis of high-grade endometrial carcinoma, Am J Surg Pathol, 37, 874, 10.1097/PAS.0b013e31827f576a Coleman, 2019, Veliparib with first-line chemotherapy and as maintenance therapy in ovarian cancer, N. Engl J Med, 381, 2403, 10.1056/NEJMoa1909707 Bartoletti, 2021, Emerging molecular alterations leading to histology-specific targeted therapies in ovarian cancer beyond PARP inhibitors, Cancer Treat Rev, 101, 10.1016/j.ctrv.2021.102298 Han, 2013, Reproducibility of histological cell type in high-grade endometrial carcinoma, Mod Pathol, 26, 1594, 10.1038/modpathol.2013.102 Clarke, 2010, Endometrial carcinoma: controversies in histopathological assessment of grade and tumour cell type, J Clin Pathol, 63, 410, 10.1136/jcp.2009.071225 Cohen, 1960, A coefficient of agreement for nominal scales, Educ Psychol Meas, 20, 37, 10.1177/001316446002000104 Patel, 2012, Interobserver agreement in the diagnosis of ovarian carcinoma types: Impact of sub-specialization, Lab Investig, 92, 292A Köbel, 2013, Biomarker-based ovarian carcinoma typing: A histologic investigation in the ovarian tumor tissue analysis consortium, Cancer Epidemiol Biomark Prev, 22, 1677, 10.1158/1055-9965.EPI-13-0391 Lujan, 2021, Dissecting the business case for adoption and implementation of digital pathology: a white paper from the Digital Pathology Association, J Pathol Inf, 12, 17, 10.4103/jpi.jpi_67_20 Coudray, 2018, Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning, Nat Med, 24, 1559, 10.1038/s41591-018-0177-5 Farnell, 2020, The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies, Histopathology, 76, 171, 10.1111/his.13991 Campanella, 2019, Clinical-grade computational pathology using weakly supervised deep learning on whole slide images, Nat Med, 25, 1301, 10.1038/s41591-019-0508-1 Bejnordi, 2017, Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer, JAMA, 318, 2199, 10.1001/jama.2017.14585 Wang, 2020, Classification of epithelial ovarian carcinoma whole-slide pathology images using deep transfer learning, Med Imaging Deep Learn, 2020, 3 Yagi, 2011, Color standardization and optimization in Whole Slide Imaging, Diagn Pathol, 6, 1, 10.1186/1746-1596-6-S1-S15 Bancroft JD, Gamble M. Theory and Practice of Histological Techniques (Elsevier Health Sciences, 2008) Lyon, 1994, Standardization of reagents and methods used in cytological and histological practice with emphasis on dyes, stains, and chromogenic reagents, Histochem J, 26, 533, 10.1007/BF00158587 Schirris, Y, Gavves, E, Nederlof, I, Horlings, HM, Teuwen, J, DeepSMILE: Self-supervised heterogeneity-aware multiple instance learning for DNA damage response defect classification directly from H&E whole-slide images, arXiv preprint (2021), https://doi.org/10.48550/arXiv.2107.09405 Ilse, 2018, Attention-based deep multiple instance learning, 35th Int Conf Mach Learn, 5, 3376 Peres, 2018, Histotype classification of ovarian carcinoma: A comparison of approaches, Gynecol Oncol, 151, 53, 10.1016/j.ygyno.2018.08.016 Turkowski K, Filters for common resampling tasks (Academic Press Professional, Inc., USA, 1990) Köbel, 2019, Ovarian carcinoma histotype: strengths and limitations of integrating morphology with immunohistochemical predictions, Int J Gynecol Pathol, 38, 353, 10.1097/PGP.0000000000000530 Boschman, 2021, The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images, J Pathol, 256, 15, 10.1002/path.5797 Reinhard, 2001, Color transfer between images, IEEE Comput Graph Appl, 21, 34, 10.1109/38.946629 Vahadane, 2016, Structure-preserving color normalization and sparse stain separation for histological images, IEEE Trans Med imaging, 35, 1962, 10.1109/TMI.2016.2529665 Macenko M, Niethammer M, Marron JS, Borland D, Woosley JT, X Guan, et al. A method for normalizing histology slides for quantitative analysis. In2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1107–1110 (2009) Paszke, 2019, Pytorch: An imperative style, high-performance deep learning library, Adv Neural Inf Process Syst, 32, 8024 He K, Zhang X, Ren S, Sun J, Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 770–778 (2016) Reddi, SJ, Kale, S, Kumar, S, On the Convergence of Adam and Beyond. arXiv preprint (2018), https://doi.org/10.48550/arXiv.1904.09237 Köbel, 2010, Diagnosis of ovarian carcinoma cell type is highly reproducible: a transcanadian study, Am J Surg Pathol, 34, 984, 10.1097/PAS.0b013e3181e1a3bb