A deep learning model to predict RNA-Seq expression of tumours from whole slide images
暂无分享,去创建一个
Pierre Courtiol | Mikhail Zaslavskiy | Charlie Saillard | Elodie Pronier | Julien Calderaro | Meriem Sefta | Sylvain Toldo | Thomas Clozel | Matahi Moarii | Gilles Wainrib | Aurélie Kamoun | Benoît Schmauch | Alberto Romagnoni | Pascale Maillé | G. Wainrib | P. Courtiol | M. Moarii | E. Pronier | M. Sefta | Sylvain Toldo | M. Zaslavskiy | T. Clozel | J. Calderaro | A. Kamoun | P. Maillé | B. Schmauch | C. Saillard | A. Romagnoni | Matahi Moarii
[1] Pascal Fua,et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Rajarsi R. Gupta,et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. , 2018, Cell reports.
[3] Ash A. Alizadeh,et al. Abstract PR09: The prognostic landscape of genes and infiltrating immune cells across human cancers , 2015 .
[4] Bram van Ginneken,et al. Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard , 2018, Scientific Reports.
[5] Clive R. Taylor,et al. Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology , 2017, The American journal of surgical pathology.
[6] Rosamaria Pinto,et al. Next-generation sequencing: advances and applications in cancer diagnosis , 2016, OncoTargets and therapy.
[7] Erin L. Schenk,et al. Targeting the Complement Pathway as a Therapeutic Strategy in Lung Cancer , 2019, Front. Immunol..
[8] E. Lai,et al. Ki-67 antigen expression in hepatocellular carcinoma using monoclonal antibody MIB1. A comparison with proliferating cell nuclear antigen. , 1995, American journal of clinical pathology.
[9] Robert L. Sutherland,et al. Cyclins and Breast Cancer , 1996, Journal of Mammary Gland Biology and Neoplasia.
[10] D. Ruderman,et al. Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens , 2018, npj Breast Cancer.
[11] P. Baldi,et al. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas , 2018, American Journal of Neuroradiology.
[12] Jakob Nikolas Kather,et al. Genomics and emerging biomarkers for immunotherapy of colorectal cancer. , 2018, Seminars in cancer biology.
[13] Jeffrey H. Chuang,et al. Pan-cancer classifications of tumor histological images using deep learning , 2019, bioRxiv.
[14] Dmitry I. Strokotov,et al. Is there a difference between T- and B-lymphocyte morphology? , 2009, Journal of biomedical optics.
[15] Andrew H. Beck,et al. Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival , 2011, Science Translational Medicine.
[16] Oumeima Laifa,et al. Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides , 2020, Hepatology.
[17] A. Ozcan,et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning , 2018, Nature Biomedical Engineering.
[18] Bram van Ginneken,et al. Automated Gleason Grading of Prostate Biopsies using Deep Learning , 2019, ArXiv.
[19] Gianluca Bontempi,et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data , 2015, Nucleic acids research.
[20] Ludmila V. Danilova,et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade , 2017, Science.
[21] Steven J. M. Jones,et al. Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.
[22] Ajay Goel,et al. Microsatellite instability in colorectal cancer. , 2010, Gastroenterology.
[23] Daniel J. Gaffney,et al. A survey of best practices for RNA-seq data analysis , 2016, Genome Biology.
[24] M. Inngjerdingen,et al. The Tetraspanin CD53 Modulates Responses from Activating NK Cell Receptors, Promoting LFA-1 Activation and Dampening NK Cell Effector Functions , 2014, PloS one.
[25] R. Kamps,et al. Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification , 2017, International journal of molecular sciences.
[26] Joel H. Saltz,et al. Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] J. Bruix,et al. Prognosis of Hepatocellular Carcinoma: The BCLC Staging Classification , 1999, Seminars in liver disease.
[28] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[29] N. Otsu. A threshold selection method from gray level histograms , 1979 .
[30] Michael R Stratton,et al. Genomics and the continuum of cancer care. , 2011, The New England journal of medicine.
[31] P. Keegan,et al. First FDA Approval Agnostic of Cancer Site - When a Biomarker Defines the Indication. , 2017, The New England journal of medicine.
[32] W. Huber,et al. which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .
[33] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] M. Sliwkowski,et al. Association of Csk-homologous Kinase (CHK) (formerly MATK) with HER-2/ErbB-2 in Breast Cancer Cells* , 1997, Journal of Biological Chemistry.
[35] H. Honda,et al. Small hepatocellular carcinoma of single nodular type: A specific reference to its surrounding cancerous area undetected radiologically and macroscopically , 1995, Journal of surgical oncology.
[36] B. van Ginneken,et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. , 2020, The Lancet. Oncology.
[37] K. Shirabe,et al. A long‐term survivor of ruptured hepatocellular carcinoma after hepatic resection , 1995, Journal of gastroenterology and hepatology.
[38] Peter Bankhead,et al. QuPath: Open source software for digital pathology image analysis , 2017, Scientific Reports.
[39] N. Kedersha,et al. Characterization of GMP-17, a granule membrane protein that moves to the plasma membrane of natural killer cells following target cell recognition. , 1996, Proceedings of the National Academy of Sciences of the United States of America.
[40] Navid Farahani,et al. A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association. , 2018, Archives of pathology & laboratory medicine.
[41] R. Pal,et al. Send Orders of Reprints at Reprints@benthamscience.net Integrated Analysis of Transcriptomic and Proteomic Data , 2022 .
[42] Yi-huan Luo,et al. Clinicopathological and prognostic significance of high Ki-67 labeling index in hepatocellular carcinoma patients: a meta-analysis. , 2015, International journal of clinical and experimental medicine.
[43] D. Hanahan,et al. Hallmarks of Cancer: The Next Generation , 2011, Cell.
[44] I. Papasotiriou,et al. Current perspectives on CHEK2 mutations in breast cancer , 2017, Breast cancer.
[45] Constantino Carlos Reyes-Aldasoro,et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study , 2019, PLoS medicine.
[46] Ernst J. Wolvetang,et al. Bone Disease - Current Knowledge and Future Prospects , 2001 .
[47] Qi Zhou,et al. CD19 and CD20 Targeted Vectors Induce Minimal Activation of Resting B Lymphocytes , 2013, PloS one.
[48] W. Huber,et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.
[49] Jakob Nikolas Kather,et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer , 2019, Nature Medicine.
[50] N. Razavian,et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.
[51] D. Koller,et al. From signatures to models: understanding cancer using microarrays , 2005, Nature Genetics.
[52] D. Brat,et al. Predicting cancer outcomes from histology and genomics using convolutional networks , 2017, Proceedings of the National Academy of Sciences.
[53] Michael C. Montalto,et al. And They Said It Couldn’t Be Done: Predicting Known Driver Mutations From H&E Slides , 2019, Journal of pathology informatics.
[54] P. Banks,et al. Novel predators emit novel cues: a mechanism for prey naivety towards alien predators , 2017, Scientific Reports.
[55] Andrew J. Schaumberg,et al. D R A F T H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer , 2017 .
[56] Angel Cruz-Roa,et al. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features , 2014, Journal of medical imaging.
[57] S. Park,et al. Deep transfer learning approach to predict tumor mutation burden (TMB) and delineate spatial heterogeneity of TMB within tumors from whole slide images , 2019, bioRxiv.
[58] P. Park,et al. A molecular portrait of microsatellite instability across multiple cancers , 2016, Nature Communications.
[59] A. Madabhushi,et al. Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer , 2018, BMC Cancer.
[60] M. Stratton,et al. The cancer genome , 2009, Nature.
[61] Mark M. Davis,et al. Identification and sequence of a fourth human T cell antigen receptor chain , 1987, Nature.
[62] S Srivastava,et al. 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. , 1998, Cancer research.
[63] M. Kudo,et al. Molecular Link between Liver Fibrosis and Hepatocellular Carcinoma , 2013, Liver Cancer.
[64] N. Linder,et al. Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples , 2016, Journal of pathology informatics.
[65] S. Nair,et al. Cell-Type-Specific Gene Expression Profiling in Adult Mouse Brain Reveals Normal and Disease-State Signatures. , 2019, Cell reports.
[66] E. Lander. Array of hope , 1999, Nature Genetics.
[67] Aung Ko Win,et al. Colorectal and other cancer risks for carriers and noncarriers from families with a DNA mismatch repair gene mutation: a prospective cohort study. , 2012, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[68] M. McCall,et al. Systematic exploration of cell morphological phenotypes associated with a transcriptomic query , 2018, Nucleic acids research.