Deep learning predicts patients outcome and mutations from digitized histology slides in gastrointestinal stromal tumor

[1]  Beatriz Garcia Gonzalez,et al.  Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy , 2021, bioRxiv.

[2]  Chung-Ming Lo,et al.  Deep Convolutional Neural Networks Detect Tumor Genotype from Pathological Tissue Images in Gastrointestinal Stromal Tumors , 2021, Cancers.

[3]  S. Sleijfer,et al.  Gastrointestinal stromal tumours: ESMO-EURACAN-GENTURIS Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2021, Annals of oncology : official journal of the European Society for Medical Oncology.

[4]  G. Litjens,et al.  Deep learning in histopathology: the path to the clinic , 2021, Nature Medicine.

[5]  Pierre Courtiol,et al.  A deep learning model to predict RNA-Seq expression of tumours from whole slide images , 2020, Nature Communications.

[6]  Soo Youn Cho,et al.  Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses , 2020, Diagnostic Pathology.

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

[8]  Alexander W. Jung,et al.  Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis , 2019, Nature Cancer.

[9]  G. Wainrib,et al.  Deep learning-based classification of mesothelioma improves prediction of patient outcome , 2019, Nature Medicine.

[10]  J. Blay,et al.  Adjuvant therapy with imatinib in gastrointestinal stromal tumors (GISTs)-review and perspectives. , 2019, Translational gastroenterology and hepatology.

[11]  Francesco Ciompi,et al.  Deep learning assisted mitotic counting for breast cancer , 2019, Laboratory Investigation.

[12]  S. Park,et al.  Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers , 2019, Korean journal of radiology.

[13]  Stephen M. Hewitt,et al.  Automated Computational Detection, Quantitation, and Mapping of Mitosis in Whole-Slide Images for Clinically Actionable Surgical Pathology Decision Support , 2019, Journal of pathology informatics.

[14]  N. Razavian,et al.  Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.

[15]  Karl Rohr,et al.  Predicting breast tumor proliferation from whole‐slide images: The TUPAC16 challenge , 2018, Medical Image Anal..

[16]  Cesare Furlanello,et al.  Evaluating reproducibility of AI algorithms in digital pathology with DAPPER , 2018, bioRxiv.

[17]  J. Blay,et al.  Improved sarcoma management in a national network of reference centers: Analysis of the NetSarc network on 13,454 patients treated between 2010 and 2014. , 2016 .

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Xiaomin Lu,et al.  Interobserver variability of mitotic index and utility of PHH3 for risk stratification in gastrointestinal stromal tumors. , 2015, American journal of clinical pathology.

[20]  J. Blay,et al.  Tumor Genotype Is an Independent Prognostic Factor in Primary Gastrointestinal Stromal Tumors of Gastric Origin: A European Multicenter Analysis Based on ConticaGIST , 2014, Clinical Cancer Research.

[21]  J. Blay,et al.  Outcome of Patients with Platelet-Derived Growth Factor Receptor Alpha–Mutated Gastrointestinal Stromal Tumors in the Tyrosine Kinase Inhibitor Era , 2012, Clinical Cancer Research.

[22]  M. Heinrich,et al.  Gastrointestinal stromal tumours: origin and molecular oncology , 2011, Nature Reviews Cancer.

[23]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  L. Terracciano,et al.  An update on molecular genetics of gastrointestinal stromal tumours , 2006, Journal of Clinical Pathology.

[25]  J. Blay,et al.  KIT mutations and dose selection for imatinib in patients with advanced gastrointestinal stromal tumours. , 2006, European journal of cancer.

[26]  J. Lasota,et al.  Gastrointestinal stromal tumors: pathology and prognosis at different sites. , 2006, Seminars in diagnostic pathology.

[27]  E. Wardelmann,et al.  Deletion of Trp‐557 and Lys‐558 in the juxtamembrane domain of the c‐kit protooncogene is associated with metastatic behavior of gastrointestinal stromal tumors , 2003, International journal of cancer.

[28]  A. D. Van den Abbeele,et al.  Efficacy and safety of imatinib mesylate in advanced gastrointestinal stromal tumors. , 2002, The New England journal of medicine.

[29]  E. Wardelmann,et al.  c-kit Mutations in Gastrointestinal Stromal Tumors Occur Preferentially in the Spindle Rather Than in the Epithelioid Cell Variant , 2002, Modern Pathology.

[30]  Sigrid Stroobants,et al.  Safety and efficacy of imatinib (STI571) in metastatic gastrointestinal stromal tumours: a phase I study , 2001, The Lancet.

[31]  S. Hirota,et al.  Gain-of-function mutations of c-kit in human gastrointestinal stromal tumors. , 1998, Science.

[32]  T. Taguchi,et al.  [Phase I study]. , 1995, Gan to kagaku ryoho. Cancer & chemotherapy.

[33]  Maya Petersen,et al.  Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. , 2015, Electronic journal of statistics.

[34]  H. Cao,et al.  Prognostic value of mutational characteristics in gastrointestinal stromal tumors: a single-center experience in 275 cases , 2013, Medical Oncology.

[35]  Jia Deng,et al.  A large-scale hierarchical image database , 2009, CVPR 2009.

[36]  G. E. Noether.  Introduction to Wilcoxon (1945) Individual Comparisons by Ranking Methods , 1992 .

[37]  Robert C. Elston,et al.  On Fisher's Method of Combining p-Values , 1991 .