A machine learning-based prognostic predictor for stage III colon cancer
A machine learning-based prognostic predictor for stage III colon cancer
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DOI：
10.1038/s41598-020-67178-0
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发表时间：
2020-06-25
影响因子：
4.6
通讯作者：
Wang, Ziqiang
中科院分区：
文献类型：
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作者：
Jiang, Dan;Liao, Junhua;Wang, Ziqiang
有限的生物标志物已被确定为III期结肠癌的预后预测因子。为了克服这一不足，我们开发了一种计算机辅助方法，将卷积神经网络与机器分类器相结合，从常规苏木精和伊红（H&E）染色的组织切片中预测III期结肠癌的预后。我们使用华西医院（WCH）的101例癌症来训练模型。通过使用来自WCH的67种癌症和来自癌症基因组图谱结肠腺癌数据库的47种癌症来验证模型的预测有效性。所选模型（梯度增强-结肠）提供的高风险与低风险复发的风险比（HR）为8.976（95%置信区间（CI），2.824-28.528; P，0.000）和10.273（95% CI，2.177-48.472; P，0.003）。预后不良组与预后良好组的HR值分别为10.687（95%CI，2.908-39.272; P，0.001）和5.033（95%CI，1.792 -14.132; P，0.002）。Gradient Boosting-Colon是一种独立的机器预后预测器，它可以直接从H&E组织切片中将III期结肠癌分层为高风险和低风险复发组，以及不良和良好预后组。我们的研究结果可以为III期结肠癌的治疗计划提供重要信息。
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.