Pan-cancer image-based detection of clinically actionable genetic alterations
暂无分享,去创建一个
Jakob Nikolas Kather | T. Luedde | P. A. van den Brandt | J. Kather | A. Pearson | D. Jäger | Jeremias Krause | H. Grabsch | H. Brenner | M. Hoffmeister | C. Trautwein | P. Bankhead | L. Kooreman | N. Cipriani | L. Heij | C. Loeffler | A. Echle | H. Muti | J. Niehues | Kai A. J. Sommer | Jefree J. Schulte | N. Ortiz-Brüchle | A. Patnaik | Andrew Srisuwananukorn | R. D. Bülow | Akash Patnaik | Nadina Ortiz-Brüchle | Lara R. Heij
[1] H. Brenner,et al. Long-term risk of colorectal cancer after negative colonoscopy. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[2] Steven J. M. Jones,et al. Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.
[3] Steven J. M. Jones,et al. Comprehensive genomic characterization of squamous cell lung cancers , 2012, Nature.
[4] Steven J. M. Jones,et al. Comprehensive molecular portraits of human breast tumors , 2012, Nature.
[5] Steven J. M. Jones,et al. Comprehensive molecular portraits of human breast tumours , 2013 .
[6] Steven J. M. Jones,et al. Comprehensive molecular characterization of gastric adenocarcinoma , 2014, Nature.
[7] A. Egloff,et al. Caspase‐8 mutations in head and neck cancer confer resistance to death receptor‐mediated apoptosis and enhance migration, invasion, and tumor growth , 2014, Molecular oncology.
[8] Steven J. M. Jones,et al. Comprehensive molecular profiling of lung adenocarcinoma , 2014, Nature.
[9] Jeffrey S. Morris,et al. The Consensus Molecular Subtypes of Colorectal Cancer , 2015, Nature Medicine.
[10] Steven J. M. Jones,et al. Genomic Classification of Cutaneous Melanoma , 2015, Cell.
[11] M. Kloor,et al. Statin use and survival after colorectal cancer: the importance of comprehensive confounder adjustment. , 2015, Journal of the National Cancer Institute.
[12] Steven J. M. Jones,et al. Comprehensive genomic characterization of head and neck squamous cell carcinomas , 2015, Nature.
[13] Kyung-Ja Cho,et al. Epithelial-Mesenchymal Transition , 2014, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.
[14] Steven J. M. Jones,et al. The Molecular Taxonomy of Primary Prostate Cancer , 2015, Cell.
[15] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Andrew Janowczyk,et al. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases , 2016, Journal of pathology informatics.
[18] Steven J. M. Jones,et al. Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma. , 2017, Cancer cell.
[19] Wenlin Huang,et al. CDC27 Induces Metastasis and Invasion in Colorectal Cancer via the Promotion of Epithelial-To-Mesenchymal Transition , 2017, Journal of Cancer.
[20] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] A. Chinnaiyan,et al. Inactivation of CDK12 Delineates a Distinct Immunogenic Class of Advanced Prostate Cancer , 2018, Cell.
[22] Chandra Sekhar Pedamallu,et al. Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas. , 2018, Cancer cell.
[23] M. Berger,et al. Clinical tumour sequencing for precision oncology: time for a universal strategy , 2018, Nature Reviews Cancer.
[24] Xiaotu Ma,et al. Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome , 2018, Nature Communications.
[25] N. Razavian,et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.
[26] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[27] 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 .
[28] Evert Bosdriesz,et al. MAP3K1 and MAP2K4 mutations are associated with sensitivity to MEK inhibitors in multiple cancer models , 2018, Cell Research.
[29] Steven J. M. Jones,et al. The Immune Landscape of Cancer , 2018, Immunity.
[30] Timo Kohlberger,et al. Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration , 2018, ArXiv.
[31] Jakob Nikolas Kather,et al. Genomics and emerging biomarkers for immunotherapy of colorectal cancer. , 2018, Seminars in cancer biology.
[32] Jakob Nikolas Kather,et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer , 2019, Nature Medicine.
[33] Jakob Nikolas Kather,et al. Deep learning detects virus presence in cancer histology , 2019, bioRxiv.
[34] Timo Kohlberger,et al. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis , 2019, Nature Medicine.
[35] N. Razavian,et al. A Deep Learning Approach for Rapid Mutational Screening in Melanoma , 2019, bioRxiv.
[36] H. Rugo,et al. Alpelisib for PIK3CA‐Mutated, Hormone Receptor–Positive Advanced Breast Cancer , 2019, The New England journal of medicine.
[37] K. Kojima,et al. A subset of diffuse-type gastric cancer is susceptible to mTOR inhibitors and checkpoint inhibitors , 2019, Journal of Experimental & Clinical Cancer Research.
[38] Constantino Carlos Reyes-Aldasoro,et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study , 2019, PLoS medicine.
[39] Thomas J. Fuchs,et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.