A machine learning-based prognostic predictor for stage III colon cancer.

Journal: Scientific reports
Published Date: Jun 25, 2020

Abstract

Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To combat this shortfall, we developed a computer-aided approach which combing convolutional neural network with machine classifier to predict the prognosis of stage III colon cancer from routinely haematoxylin and eosin (H&E) stained tissue slides. We trained the model by using 101 cancers from West China Hospital (WCH). The predictive effectivity of the model was validated by using 67 cancers from WCH and 47 cancers from The Cancer Genome Atlas Colon Adenocarcinoma database. The selected model (Gradient Boosting-Colon) provided a hazard ratio (HR) for high- vs. low-risk recurrence of 8.976 (95% confidence interval (CI), 2.824-28.528; P, 0.000), and 10.273 (95% CI, 2.177-48.472; P, 0.003) in the two test groups, from the multivariate Cox proportional hazards analysis. It gave a HR value of 10.687(95% CI, 2.908-39.272; P, 0.001) and 5.033 (95% CI,1.792-14.132; P, 0.002) for the poor vs. good prognosis groups. Gradient Boosting-Colon is an independent machine prognostic predictor which allows stratification of stage III colon cancer into high- and low-risk recurrence groups, and poor and good prognosis groups directly from the H&E tissue slides. Our findings could provide crucial information to aid treatment planning during stage III colon cancer.

Authors

  • Dan Jiang
    Department of Operative Dentistry and Endodontics, The Affiliated Hospital of Stomatology, Chongqing Medical University, Chongqing, China.
  • Junhua Liao
    College of Computer Science, Sichuan University, Chengdu, China.
  • Haihan Duan
    College of Computer Science, Sichuan University, South Section 1, Yihuan Road, Chengdu, 610065, Sichuan, China.
  • Qingbin Wu
    Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China.
  • Gemma Owen
    Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
  • Chang Shu
    Department of Pharmaceutical Analysis, School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China.
  • Liangyin Chen
    College of Computer Science, Sichuan University, South Section 1, Yihuan Road, Chengdu, 610065, Sichuan, China. chenliangyin@scu.edu.cn.
  • Yanjun He
    Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.
  • Ziqian Wu
  • Du He
    Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.
  • Wenyan Zhang
    Department of Pathology, West China Hospital, Sichuan University, Chengdu, China. zhangwenyanpath@163.com.
  • Ziqiang Wang
    Research Center of Clinical Laboratory Science, Bengbu Medical University, Bengbu, China.

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