Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts.

[1]  T. Brinker,et al.  Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application , 2022, ESMO open.

[2]  M. Hung,et al.  Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. , 2022, The Lancet. Digital health.

[3]  M. Köbel,et al.  Selection of endometrial carcinomas for p53 immunohistochemistry based on nuclear features , 2021, The journal of pathology. Clinical research.

[4]  Dimitris N. Metaxas,et al.  Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning , 2021, npj Precision Oncology.

[5]  H. Horlings,et al.  DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer , 2021, Medical Image Anal..

[6]  H. Mackay,et al.  Tertiary lymphoid structures critical for prognosis in endometrial cancer patients , 2021, Nature Communications.

[7]  B. Martin,et al.  Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. , 2021, The Lancet. Oncology.

[8]  Cyrus Chargari,et al.  ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma , 2020, International Journal of Gynecological Cancer.

[9]  Anne L. Martel,et al.  Self supervised contrastive learning for digital histopathology , 2020, Machine Learning with Applications.

[10]  R. Socher,et al.  Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains , 2020, Nature Communications.

[11]  K. Mertz,et al.  Prognostic Integrated Image-Based Immune and Molecular Profiling in Early-Stage Endometrial Cancer , 2020, Cancer Immunology Research.

[12]  H. Putter,et al.  Molecular Classification of the PORTEC-3 Trial for High-Risk Endometrial Cancer: Impact on Prognosis and Benefit From Adjuvant Therapy , 2020, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[13]  K. Sirinukunwattana,et al.  Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning , 2020, Gut.

[14]  Ming Y. Lu,et al.  Data-efficient and weakly supervised computational pathology on whole-slide images , 2020, Nature Biomedical Engineering.

[15]  Kaiming He,et al.  Improved Baselines with Momentum Contrastive Learning , 2020, ArXiv.

[16]  N. Razavian,et al.  Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models , 2020, bioRxiv.

[17]  A. Madabhushi,et al.  HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides. , 2019, JCO clinical cancer informatics.

[18]  Jin Tae Kwak,et al.  Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images , 2018, Medical Image Anal..

[19]  H. Putter,et al.  Adjuvant chemoradiotherapy versus radiotherapy alone for women with high-risk endometrial cancer (PORTEC-3): final results of an international, open-label, multicentre, randomised, phase 3 trial , 2018, The Lancet. Oncology.

[20]  H. Putter,et al.  Improved Risk Assessment by Integrating Molecular and Clinicopathological Factors in Early-stage Endometrial Cancer—Combined Analysis of the PORTEC Cohorts , 2016, Clinical Cancer Research.

[21]  P. Pollock,et al.  Refining prognosis and identifying targetable pathways for high-risk endometrial cancer; a TransPORTEC initiative , 2015, Modern Pathology.

[22]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[23]  Steven J. M. Jones,et al.  Integrated genomic characterization of endometrial carcinoma , 2013, Nature.

[24]  Benjamin E. Gross,et al.  The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. , 2012, Cancer discovery.

[25]  S. Siesling,et al.  Outcome of Endometrial Cancer Stage IIIA with Adnexa or Serosal Involvement Only , 2011, Obstetrics and gynecology international.

[26]  H. Putter,et al.  Vaginal brachytherapy versus pelvic external beam radiotherapy for patients with endometrial cancer of high-intermediate risk (PORTEC-2): an open-label, non-inferiority, randomised trial , 2010, The Lancet.

[27]  S. Siesling,et al.  Multicenter cohort study on treatment results and risk factors in stage II endometrial carcinoma , 2007, International Journal of Gynecologic Cancer.

[28]  P. Koper,et al.  Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial , 2000, The Lancet.

[29]  T. D. Hamilton,et al.  Classification of Tumours , 1930, Edinburgh medical journal.