Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning
Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning
Abstract Visual analysis of histopathology slides of lung cell tissues is one of the main methods used by pathologists to assess the stage, types and sub-types of lung cancers. Adenocarcinoma and squamous cell carcinoma are two most prevalent sub-types of lung cancer, but their distinction can be challenging and time-consuming even for the expert eye. In this study, we trained a deep learning convolutional neural network (CNN) model (inception v3) on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately classify whole-slide pathology images into adenocarcinoma, squamous cell carcinoma or normal lung tissue. Our method slightly outperforms a human pathologist, achieving better sensitivity and specificity, with ～0.97 average Area Under the Curve (AUC) on a held-out population of whole-slide scans. Furthermore, we trained the neural network to predict the ten most commonly mutated genes in lung adenocarcinoma. We found that six of these genes – STK11, EGFR, FAT1, SETBP1, KRAS and TP53 – can be predicted from pathology images with an accuracy ranging from 0.733 to 0.856, as measured by the AUC on the held-out population. These findings suggest that deep learning models can offer both specialists and patients a fast, accurate and inexpensive detection of cancer types or gene mutations, and thus have a significant impact on cancer treatment.
Feny? David、Sakellaropoulos Theodore、Razavian Narges、Tsirigos Aristotelis、Moreira Andre L.、Coudray Nicolas
Institute for Systems Genetics, New York University School of Medicine||Department of Biochemistry and molecular Pharmacology, New York University School of MedicineSchool of Mechanical Engineering, National Technical University of AthensDepartment of Population Health and the Center for Healthcare Innovation and Delivery Science, New York University School of MedicineApplied Bioinformatics Laboratories, New York University School of Medicine||Department of Pathology, New York University School of MedicineDepartment of Pathology, New York University School of Medicine||Center for Biospecimen Research and Development, New York UniversityApplied Bioinformatics Laboratories, New York University School of Medicine||Skirball Institue, Dept. of Cell Biology, New York University School of Medicine
肿瘤学医学研究方法生物科学研究方法、生物科学研究技术
Computational BiologyCancerPrecision MedicineImage AnalysisComputer Vision and Pattern RecognitionrQuantitative MethodsDeep-learning
Feny? David,Sakellaropoulos Theodore,Razavian Narges,Tsirigos Aristotelis,Moreira Andre L.,Coudray Nicolas.Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning[EB/OL].(2025-03-28)[2025-08-05].https://www.biorxiv.org/content/10.1101/197574.点此复制
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