Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images
暂无分享,去创建一个
Lingdao Sha | Bolesław L Osinski | Irvin Ho | Timothy Tan | Caleb Willis | Hannah Weiss | N. Beaubier | B. Mahon | T. Taxter | S. Yip | Bolesław L. Osinski
[1] J. Pignon,et al. Is concurrent chemoradiation the standard of care for locally advanced non-small cell lung cancer? A review of guidelines and evidence , 2010 .
[2] O. Adeyi,et al. Pathology services in developing countries-the West African experience. , 2011, Archives of pathology & laboratory medicine.
[3] A. Chang. Chemotherapy, chemoresistance and the changing treatment landscape for NSCLC. , 2011, Lung cancer.
[4] C. Drake,et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. , 2012, The New England journal of medicine.
[5] Alexis B. Carter,et al. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. , 2013, Archives of pathology & laboratory medicine.
[6] Ada T Feldman,et al. Tissue processing and hematoxylin and eosin staining. , 2014, Methods in molecular biology.
[7] R. Herbst,et al. Programmed death ligand-1 expression in non-small cell lung cancer , 2014, Laboratory Investigation.
[8] H. Kohrt,et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients , 2014, Nature.
[9] R. Cardiff,et al. Manual hematoxylin and eosin staining of mouse tissue sections. , 2014, Cold Spring Harbor protocols.
[10] L. Pusztai,et al. PD-L1 Expression Correlates with Tumor-Infiltrating Lymphocytes and Response to Neoadjuvant Chemotherapy in Breast Cancer , 2014, Cancer Immunology Research.
[11] K. Kiel,et al. Patterns of locoregional failure in stage III non-small cell lung cancer treated with definitive chemoradiation therapy. , 2014, Practical radiation oncology.
[12] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[13] C. Rudin,et al. Nivolumab versus Docetaxel in Advanced Nonsquamous Non-Small-Cell Lung Cancer. , 2015, The New England journal of medicine.
[14] Antoni Ribas,et al. Classifying Cancers Based on T-cell Infiltration and PD-L1. , 2015, Cancer research.
[15] Razelle Kurzrock,et al. PD-L1 Expression as a Predictive Biomarker in Cancer Immunotherapy , 2015, Molecular Cancer Therapeutics.
[16] Zhaoqin Huang,et al. Predictive biomarkers in PD-1/PD-L1 checkpoint blockade immunotherapy. , 2015, Cancer treatment reviews.
[17] A. Chella,et al. PD-1 and PD-L1 expression in molecularly selected non-small-cell lung cancer patients , 2014, British Journal of Cancer.
[18] B. Cho,et al. Nivolumab in NSCLC: latest evidence and clinical potential , 2015, Therapeutic advances in medical oncology.
[19] A. Scorilas,et al. Evaluation of PD-L1 Expression and Associated Tumor-Infiltrating Lymphocytes in Laryngeal Squamous Cell Carcinoma , 2015, Clinical Cancer Research.
[20] F. Hirsch,et al. Programmed Death Ligand-1 Immunohistochemistry: Friend or Foe? , 2016, Archives of pathology & laboratory medicine.
[21] Nico Karssemeijer,et al. Stain Specific Standardization of Whole-Slide Histopathological Images , 2016, IEEE Transactions on Medical Imaging.
[22] Gideon Blumenthal,et al. FDA Approval Summary: Nivolumab for the Treatment of Metastatic Non-Small Cell Lung Cancer With Progression On or After Platinum-Based Chemotherapy , 2016, The oncologist.
[23] B. van Ginneken,et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis , 2016, Scientific Reports.
[24] M. Dolled-Filhart,et al. Development of a Companion Diagnostic PD-L1 Immunohistochemistry Assay for Pembrolizumab Therapy in Non–Small-cell Lung Cancer , 2016, Applied immunohistochemistry & molecular morphology : AIMM.
[25] Carsten Denkert,et al. Standardized evaluation of tumor-infiltrating lymphocytes in breast cancer: results of the ring studies of the international immuno-oncology biomarker working group , 2016, Modern Pathology.
[26] Y. Shentu,et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial , 2016, The Lancet.
[27] Nassir Navab,et al. Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images , 2016, IEEE Transactions on Medical Imaging.
[28] R. Herbst,et al. Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non-Small-Cell Lung Cancer. , 2016, JAMA oncology.
[29] P. Keegan,et al. FDA Approval Summary: Pembrolizumab for the Treatment of Patients With Metastatic Non-Small Cell Lung Cancer Whose Tumors Express Programmed Death-Ligand 1 , 2016, The oncologist.
[30] James W. Smith,et al. Development of immunohistochemistry services for cancer care in western Kenya: Implications for low- and middle-income countries , 2016, African journal of laboratory medicine.
[31] O. Abdel-Rahman. Correlation between PD-L1 expression and outcome of NSCLC patients treated with anti-PD-1/PD-L1 agents: A meta-analysis. , 2016, Critical reviews in oncology/hematology.
[32] Alan Sharpe,et al. Agreement between Programmed Cell Death Ligand-1 Diagnostic Assays across Multiple Protein Expression Cutoffs in Non–Small Cell Lung Cancer , 2017, Clinical Cancer Research.
[33] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[34] J. Taube,et al. A Prospective, Multi-institutional, Pathologist-Based Assessment of 4 Immunohistochemistry Assays for PD-L1 Expression in Non–Small Cell Lung Cancer , 2017, JAMA oncology.
[35] Peter Bankhead,et al. QuPath: Open source software for digital pathology image analysis , 2017, Scientific Reports.
[36] A. Kitano,et al. Tumour-infiltrating lymphocytes are correlated with higher expression levels of PD-1 and PD-L1 in early breast cancer , 2017, ESMO Open.
[37] Andrew H. Beck,et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.
[38] T. Čufer,et al. PD-L1 Expression in Squamous-cell Carcinoma and Adenocarcinoma of the Lung , 2017, Radiology and oncology.
[39] K. Bloom,et al. Programmed Death-Ligand 1 Immunohistochemistry Testing: A Review of Analytical Assays and Clinical Implementation in Non-Small-Cell Lung Cancer. , 2017, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[40] Heather D. Couture,et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype , 2018, npj Breast Cancer.
[41] N. Razavian,et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.
[42] D. Brat,et al. Predicting cancer outcomes from histology and genomics using convolutional networks , 2017, Proceedings of the National Academy of Sciences.
[43] A. Jemal,et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.
[44] Tomasz Markiewicz,et al. Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides , 2019, Scientific Reports.