Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

The Lancet Digital Health - Tập 3 - Trang e654-e664 - 2021
Hannah Sophie Muti1, Lara Rosaline Heij2,3, Gisela Keller4, Meike Kohlruss4, Rupert Langer5,6, Bastian Dislich5, Jae-Ho Cheong7, Young-Woo Kim8, Hyunki Kim9, Myeong-Cherl Kook10, David Cunningham11, William H Allum12, Ruth E Langley13, Matthew G Nankivell13, Philip Quirke14, Jeremy D Hayden15, Nicholas P West14, Andrew J Irvine14, Takaki Yoshikawa16, Takashi Oshima17
1Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
2Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany
3Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
4Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
5Institute of Pathology, Inselspital, University of Bern, Switzerland
6Institute of Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University Linz, Linz, Austria
7Department of Surgery, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea
8Center for Gastric Cancer, National Cancer Center, Goyang, South Korea
9Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea
10Department of Pathology, National Cancer Center, Goyang, South Korea
11Department of Medicine, Gastrointestinal and Lymphoma Units, The Royal Marsden NHS Foundation Trust, London, UK
12Department of Surgery, Royal Marsden Hospital, London, UK
13Medical Research Council Clinical Trials Unit, University College London, London, UK
14Pathology and Data Analytics, Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, UK
15Department of Oesophago-Gastric Surgery, St James's University Hospital, Leeds, UK
16Department of Gastric Surgery, National Cancer Center Hospital, Tokyo, Japan
17Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Yokohama, Japan

Tài liệu tham khảo

Bray, 2018, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin, 68, 394, 10.3322/caac.21492 De Mello, 2019, Current and future aspects of immunotherapy for esophageal and gastric malignancies, Am Soc Clin Oncol Educ Book, 39, 237, 10.1200/EDBK_236699 Kim, 2018, Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer, Nat Med, 24, 1449, 10.1038/s41591-018-0101-z Kohlruss, 2019, Prognostic implication of molecular subtypes and response to neoadjuvant chemotherapy in 760 gastric carcinomas: role of Epstein-Barr virus infection and high- and low-microsatellite instability, J Pathol Clin Res, 5, 227, 10.1002/cjp2.137 Pietrantonio, 2019, Individual patient data meta-analysis of the value of microsatellite instability as a biomarker in gastric cancer, J Clin Oncol, 37, 3392, 10.1200/JCO.19.01124 Kim, 2015, Deregulation of immune response genes in patients with Epstein-Barr virus-associated gastric cancer and outcomes, Gastroenterology, 148, 137, 10.1053/j.gastro.2014.09.020 Roh, 2019, Single patient classifier assay, microsatellite instability, and Epstein-Barr virus status predict clinical outcomes in stage II/III gastric cancer: results from CLASSIC trial, Yonsei Med J, 60, 132, 10.3349/ymj.2019.60.2.132 2014, Comprehensive molecular characterization of gastric adenocarcinoma, Nature, 513, 202, 10.1038/nature13480 Boland, 1998, A National Cancer Institute workshop on microsatellite instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer, Cancer Res, 58, 5248 Gulley, 2001, Molecular diagnosis of Epstein-Barr virus-related diseases, J Mol Diagn, 3, 1, 10.1016/S1525-1578(10)60642-3 Kather, 2019, Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer, Nat Med, 25, 1054, 10.1038/s41591-019-0462-y Kather, 2020, Development of AI-based pathology biomarkers in gastrointestinal and liver cancer, Nat Rev Gastroenterol Hepatol, 17, 591, 10.1038/s41575-020-0343-3 Fu, 2020, Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis, Nat Cancer, 1, 800, 10.1038/s43018-020-0085-8 Kather, 2020, Pan-cancer image-based detection of clinically actionable genetic alterations, Nat Cancer, 1, 789, 10.1038/s43018-020-0087-6 Schmauch, 2020, A deep learning model to predict RNA-Seq expression of tumours from whole slide images, Nat Commun, 11, 10.1038/s41467-020-17678-4 Echle, 2020, Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning, Gastroenterology, 159, 1406, 10.1053/j.gastro.2020.06.021 Yamashita, 2021, Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study, Lancet Oncol, 22, 132, 10.1016/S1470-2045(20)30535-0 Bilal, 2021, Novel deep learning algorithm predicts the status of molecular pathways and key mutations in colorectal cancer from routine histology images, medRxiv Yamashita, 2021, Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation, arXiv Wang, 2020, Microsatellite instability prediction of uterine corpus endometrial carcinoma based on H&E histology whole-slide imaging, 1289 Ke, 2020, A prediction model of microsatellite status from histology images, 334 Song, 2020, Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning, Nat Commun, 11, 10.1038/s41467-020-18147-8 Dislich, 2020, Preservation of Epstein-Barr virus status and mismatch repair protein status along the metastatic course of gastric cancer, Histopathology, 76, 740, 10.1111/his.14059 Bang, 2012, Adjuvant capecitabine and oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): a phase 3 open-label, randomised controlled trial, Lancet, 379, 315, 10.1016/S0140-6736(11)61873-4 Cunningham, 2006, Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer, N Engl J Med, 355, 11, 10.1056/NEJMoa055531 Hayashi, 2013, The superiority of the seventh edition of the TNM classification depends on the overall survival of the patient cohort: comparative analysis of the sixth and seventh TNM editions in patients with gastric cancer from Japan and the United Kingdom, Cancer, 119, 1330, 10.1002/cncr.27928 Polom, 2019, KRAS mutation in gastric cancer and prognostication associated with microsatellite instability status, Pathol Oncol Res, 25, 333, 10.1007/s12253-017-0348-6 Schlößer, 2015, Immune checkpoints programmed death 1 ligand 1 and cytotoxic T lymphocyte associated molecule 4 in gastric adenocarcinoma, OncoImmunology, 5 Liu, 2018, Comparative molecular analysis of gastrointestinal adenocarcinomas, Cancer Cell, 33, 721, 10.1016/j.ccell.2018.03.010 Bossuyt, 2015, STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies, BMJ, 351 Muti Dinis-Ribeiro, 2012, Endoscopy, 44, 74, 10.1055/s-0031-1291491 Bankhead, 2017, QuPath: open source software for digital pathology image analysis, Sci Rep, 7, 10.1038/s41598-017-17204-5 Macenko, 2009, A method for normalizing histology slides for quantitative analysis, 1107 Zhang, 2017, ShuffleNet: an extremely efficient convolutional neural network for mobile devices, arXiv Echle, 2020, Deep learning in cancer pathology: a new generation of clinical biomarkers, Br J Cancer, 124, 686, 10.1038/s41416-020-01122-x McCradden, 2020, Ethical limitations of algorithmic fairness solutions in health care machine learning, Lancet Digit Health, 2, e221, 10.1016/S2589-7500(20)30065-0 Fridman, 2012, The immune contexture in human tumours: impact on clinical outcome, Nat Rev Cancer, 12, 298, 10.1038/nrc3245 Smyth, 2020, Gastric cancer, Lancet, 396, 635, 10.1016/S0140-6736(20)31288-5