机构：[1]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.四川大学华西医院[2]Sichuan University- University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, West China Hospital, Sichuan University, Chengdu, China.四川大学华西医院[3]Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China.四川大学华西医院[4]College of Computer Science, Sichuan University, Chengdu, China.[5]The Institute for Industrial Internet Research, Sichuan University, Chengdu, China.[6]State Key Laboratory of Biotherapy and Cancer Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.四川大学华西医院[7]Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.[8]West China College of Stomatology, Sichuan University, Chengdu, China.
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.
基金：
This work was supported by the National Natural Science Foundation of China (No. 81401990) from Dan Jiang.
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2020]版：
大类|3 区综合性期刊
小类|3 区综合性期刊
最新[2023]版：
大类|2 区综合性期刊
小类|2 区综合性期刊
第一作者：
第一作者机构：[1]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.[2]Sichuan University- University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, West China Hospital, Sichuan University, Chengdu, China.
共同第一作者：
通讯作者：
通讯机构：[1]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.[2]Sichuan University- University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, West China Hospital, Sichuan University, Chengdu, China.[3]Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China.
推荐引用方式(GB/T 7714)：
Dan Jiang,Junhua Liao,Haihan Duan,et al.A machine learning-based prognostic predictor for stage III colon cancer.[J].Scientific reports.2020,10(1):10333.doi:10.1038/s41598-020-67178-0.
APA：
Dan Jiang,Junhua Liao,Haihan Duan,Qingbin Wu,Gemma Owen...&Ziqiang Wang.(2020).A machine learning-based prognostic predictor for stage III colon cancer..Scientific reports,10,(1)
MLA：
Dan Jiang,et al."A machine learning-based prognostic predictor for stage III colon cancer.".Scientific reports 10..1(2020):10333
