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Sakellaropoulos, Theodore; Tsirigos, Aristotelis; Razavian, Narges; Fenyo, David; Moreira, Andre; Coudray, Nicolas (2017)
Types: Preprint
Subjects:
Identifiers:doi:10.1101/197574
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
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • American Cancer Society, (2017).
    • Chan, B. A. & Hughes, B. G. Targeted therapy for non-small cell lung cancer: current standards and the promise of the future. Translational Lung Cancer Research 4, 36-54 (2015).
    • Terra, S. B. et al. Molecular characterization of pulmonary sarcomatoid carcinoma: analysis of 33 cases. Modern Pathology 29, 824-831 (2016).
    • Blumenthal, G. M. et al. Oncology Drug Approvals: Evaluating Endpoints and Evidence in an Era of Breakthrough Therapie. The Oncologist 22, 762-767 (2017).
    • J√§nne, P. A. et al. Selumetinib plus docetaxel for KRAS-mutant advanced non-small-cell lung cancer: a randomised, multicentre, placebo-controlled, phase 2 study. The Lancet Oncology 14, 38-47 (2013).
    • Luo, X. et al. Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis. Journal of Thoracic Oncology 12, 501-509 (2017).
    • Yu, K.-H. et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nature Communications 7 (2016).
    • Schmidhuber, J. Deep learning in neural networks: An overview. Neural Networks 61, 85-117 (2015).
    • Neural Computation 18, 1527-1554 (2006).
    • Greenspan, H., Ginneken, B. v. & Summers, R. M. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE TRANSACTIONS ON MEDICAL IMAGING 35, 1153-1159 (2016).
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