Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

[1]  D. Kong,et al.  Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm , 2022, PloS one.

[2]  D. Schadendorf,et al.  Tertiary lymphoid structures improve immunotherapy and survival in melanoma , 2020, Nature.

[3]  Jakob Nikolas Kather,et al.  Pan-cancer image-based detection of clinically actionable genetic alterations , 2019, Nature Cancer.

[4]  Anne E Carpenter,et al.  Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl , 2019, Nature Methods.

[5]  Peiling Tsou,et al.  Mapping Driver Mutations to Histopathological Subtypes in Papillary Thyroid Carcinoma: Applying a Deep Convolutional Neural Network , 2019, Journal of clinical medicine.

[6]  Alberto Romagnoni,et al.  Transcriptomic learning for digital pathology , 2019, bioRxiv.

[7]  Thomas J. Fuchs,et al.  Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.

[8]  Jakob Nikolas Kather,et al.  Deep learning detects virus presence in cancer histology , 2019, bioRxiv.

[9]  Jens Rittscher,et al.  Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning , 2019, bioRxiv.

[10]  Jakob Nikolas Kather,et al.  Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer , 2019, Nature Medicine.

[11]  Geert J. S. Litjens,et al.  Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology , 2019, Medical Image Anal..

[12]  Daniel Smilkov,et al.  Similar image search for histopathology: SMILY , 2019, npj Digital Medicine.

[13]  D. Geschwind,et al.  Single-cell in situ transcriptomic map of astrocyte cortical layer diversity , 2018, bioRxiv.

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

[15]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection , 2018, J. Open Source Softw..

[16]  M. Stratton,et al.  Universal Patterns of Selection in Cancer and Somatic Tissues , 2018, Cell.

[17]  P. Baldi,et al.  Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas , 2018, American Journal of Neuroradiology.

[18]  Yuan Ji,et al.  Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types , 2018, bioRxiv.

[19]  Ashton C. Berger,et al.  Genomic and Functional Approaches to Understanding Cancer Aneuploidy. , 2018, Cancer cell.

[20]  Rajarsi R. Gupta,et al.  Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. , 2018, Cell reports.

[21]  Steven J. M. Jones,et al.  The Immune Landscape of Cancer , 2018, Immunity.

[22]  Joel H Saltz,et al.  PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective , 2018, The Journal of pathology.

[23]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[24]  R. Altman,et al.  Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma. , 2017, Cell systems.

[25]  Chuang Tan,et al.  Universal Patterns of Selection in Cancer and Somatic Tissues , 2018, Cell.

[26]  Steven J. M. Jones,et al.  Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas , 2017, Cell.

[27]  Qianjin Feng,et al.  Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis. , 2017, Cancer research.

[28]  D. Brat,et al.  Predicting cancer outcomes from histology and genomics using convolutional networks , 2017, Proceedings of the National Academy of Sciences.

[29]  A. Børresen-Dale,et al.  Breast Cancer Molecular Stratification: From Intrinsic Subtypes to Integrative Clusters. , 2017, The American journal of pathology.

[30]  Patrick Rubin-Delanchy,et al.  Choosing between methods of combining p-values , 2017, 1707.06897.

[31]  Benjamin J. Raphael,et al.  The evolutionary history of 2,658 cancers , 2017, Nature.

[32]  Y. Lévy,et al.  Corrigendum: CD32a is a marker of a CD4 T-cell HIV reservoir harbouring replication-competent proviruses , 2017, Nature.

[33]  S. Thrun,et al.  Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[34]  Mithat Gönen,et al.  Morphological characterization of colorectal cancers in The Cancer Genome Atlas reveals distinct morphology–molecular associations: clinical and biological implications , 2017, Modern Pathology.

[35]  J. Guinney,et al.  Erratum: Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer (Nature reviews. Cancer (2017) 17 2 (79-92)) , 2017 .

[36]  J. Guinney,et al.  Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer , 2017, Nature Reviews Cancer.

[37]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[38]  J. Guinney,et al.  Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer , 2017, Nature Reviews Cancer.

[39]  Samy Bengio,et al.  Understanding deep learning requires rethinking generalization , 2016, ICLR.

[40]  Allison P. Heath,et al.  Toward a Shared Vision for Cancer Genomic Data. , 2016, The New England journal of medicine.

[41]  Ce Zhang,et al.  Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features , 2016, Nature Communications.

[42]  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 .

[43]  Patrik L. Ståhl,et al.  Visualization and analysis of gene expression in tissue sections by spatial transcriptomics , 2016, Science.

[44]  David C. Jones,et al.  Landscape of somatic mutations in 560 breast cancer whole genome sequences , 2016, Nature.

[45]  R. Gibbs,et al.  Genomic analyses identify molecular subtypes of pancreatic cancer , 2016, Nature.

[46]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[47]  G. Sauter,et al.  Partial PTEN deletion is linked to poor prognosis in breast cancer , 2015, BMC Cancer.

[48]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[49]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[50]  Steven J. M. Jones,et al.  The Molecular Taxonomy of Primary Prostate Cancer , 2015, Cell.

[51]  Sidra Nawaz,et al.  Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer , 2015, Modern Pathology.

[52]  Francisco Beca,et al.  Altered PPP2R2A and Cyclin D1 expression defines a subgroup of aggressive luminal-like breast cancer , 2015, BMC Cancer.

[53]  J. Elmore,et al.  Diagnostic concordance among pathologists interpreting breast biopsy specimens. , 2015, JAMA.

[54]  Adam A. Margolin,et al.  Assessing the clinical utility of cancer genomic and proteomic data across tumor types , 2014, Nature Biotechnology.

[55]  Carlos Caldas,et al.  TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance , 2014, Clinical Cancer Research.

[56]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[57]  S. Gabriel,et al.  Pan-cancer patterns of somatic copy-number alteration , 2013, Nature Genetics.

[58]  Carolina Wählby,et al.  In situ sequencing for RNA analysis in preserved tissue and cells , 2013, Nature Methods.

[59]  Robert Brian Jenkins,et al.  Molecular Testing Guideline for Selection of Lung Cancer Patients for EGFR and ALK Tyrosine Kinase Inhibitors: Guideline from the College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology , 2013, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[60]  G. Giaccone,et al.  Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors: guideline from the College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology. , 2013, Archives of pathology & laboratory medicine.

[61]  F. Markowetz,et al.  Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling , 2012, Science Translational Medicine.

[62]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[63]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.

[64]  S. Påhlman,et al.  Cancer cell differentiation heterogeneity and aggressive behavior in solid tumors , 2012, Upsala journal of medical sciences.

[65]  Andrea J. O'Hara,et al.  The genomics and genetics of endometrial cancer. , 2012, Advances in genomics and genetics.

[66]  K. Aldape,et al.  New strategies in melanoma: molecular testing in advanced disease. , 2012, Clinical cancer research : an official journal of the American Association for Cancer Research.

[67]  Rosette Lidereau,et al.  PIK3CA mutation impact on survival in breast cancer patients and in ERα, PR and ERBB2-based subgroups , 2012, Breast Cancer Research.

[68]  Yu Cheng,et al.  Evaluation of PPP2R2A as a prostate cancer susceptibility gene: a comprehensive germline and somatic study. , 2011, Cancer genetics.

[69]  G. D. de Bock,et al.  The prognostic influence of tumour-infiltrating lymphocytes in cancer: a systematic review with meta-analysis , 2011, British Journal of Cancer.

[70]  Trevor Hastie,et al.  Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. , 2011, Journal of statistical software.

[71]  Bin Wang,et al.  Deconvolution Estimation in Measurement Error Models: The R Package decon. , 2011, Journal of statistical software.

[72]  Payal Sipahimalani,et al.  A Histology-Based Model for Predicting Microsatellite Instability in Colorectal Cancers , 2010, The American journal of surgical pathology.

[73]  M. Pollheimer,et al.  Tumor necrosis is a new promising prognostic factor in colorectal cancer. , 2010, Human pathology.

[74]  M. Aubry,et al.  Diagnostic concordance of histologic lung cancer type between bronchial biopsy and cytology specimens taken during the same bronchoscopic procedure. , 2010, Archives of pathology & laboratory medicine.

[75]  C. Perou,et al.  Allele-specific copy number analysis of tumors , 2010, Proceedings of the National Academy of Sciences.

[76]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[77]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[78]  Yoram Singer,et al.  Efficient Learning using Forward-Backward Splitting , 2009, NIPS.

[79]  Tara L. Naylor,et al.  Characterization CSMD1 in a large set of primary lung, head and neck, breast and skin cancer tissues , 2009, Cancer biology & therapy.

[80]  J. Manola,et al.  TP53 mutations and survival in squamous-cell carcinoma of the head and neck. , 2007, The New England journal of medicine.

[81]  Ming Tan,et al.  Molecular mechanisms of erbB2-mediated breast cancer chemoresistance. , 2007, Advances in experimental medicine and biology.

[82]  Electron Kebebew,et al.  The Prevalence and Prognostic Value of BRAF Mutation in Thyroid Cancer , 2007, Annals of surgery.

[83]  B. Scheithauer,et al.  The 2007 WHO Classification of Tumours of the Central Nervous System , 2007, Acta Neuropathologica.

[84]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[85]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[86]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[87]  K. Aldape,et al.  Small Cell Architecture—A Histological Equivalent of EGFR Amplification in Glioblastoma Multiforme? , 2001, Journal of neuropathology and experimental neurology.

[88]  F. Harrell,et al.  Evaluating the yield of medical tests. , 1982, JAMA.

[89]  D. E. Roberts,et al.  The Upper Tail Probabilities of Spearman's Rho , 1975 .

[90]  E. S. Pearson,et al.  TESTS FOR RANK CORRELATION COEFFICIENTS. I , 1957 .

[91]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[92]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[93]  A. Madabhushi,et al.  Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology , 2019, Nature reviews. Clinical oncology.

[94]  藤倉雄二,et al.  わが国における成人市中肺炎原因微生物についてのsystematic review/meta‐analysis , 2016 .

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

[96]  Sidra Nawaz,et al.  Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer , 2015, Modern Pathology.

[97]  Yichuan Zhang,et al.  Advances in Neural Information Processing Systems 25 , 2012 .

[98]  T. Ulbright,et al.  The Pathologist's Perspective , 1999 .

[99]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

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

[101]  D. Cox.  Regression Models and Life-Tables , 1972 .