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Abstract：
Machine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling.
This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival.
The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance.
A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival.
This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.
This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival.
The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance.
A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival.
This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.
摘要：
背景：机器学习（ML）方法在预测结直肠癌（CRC）生存率方面显示出巨大的潜力。然而,到目前为止，引入的ML模型主要集中在二元结果上，并没有考虑这种类型的建模的时间到事件的性质。
目的：本研究旨在评估ML方法对时间至事件生存数据建模的性能，并开发用于预测CRC特异性生存的透明模型。
方法：本回顾性队列研究中使用的数据集包含2012年12月28日至2019年12月27日在华西医院新诊断为CRC的患者的信息。四川大学。我们评估了6个代表性ML模型的性能，包括随机生存森林(RSF)，梯度增压机（GBM），DeepSurv,DeepHit,神经网络扩展的时间相关Cox(或Cox-Time)，和神经多任务逻辑回归（N-MTLR）预测CRC特异性生存率。采用链式方程的多重插补方法来处理变量中的缺失值。多变量分析和临床经验用于选择与CRC生存相关的重要特征。使用时间依赖性一致性指数在重复5次的分层5倍交叉验证中评估模型性能，综合Brier评分,校正曲线,和决策曲线。采用Shapley加法扩展方法计算特征重要性。
结果：本研究共纳入2157例CRC患者。在6种时间到事件ML模型中，DeepHit模型表现出最佳的辨别能力（时间依赖性一致性指数0.789，95％CI0.779-0.799），RSF模型产生了更好的校准生存估计（综合Brier评分0.096，95％CI0.094-0.099），但这些并不具有统计学意义。此外,RSF,GBM,DeepSurv,考克斯时间,和N-MTLR模型在辨别和校准方面具有与Cox比例风险模型相当的预测准确性。校准曲线显示所有ML模型均表现出良好的5年生存校准。5年CRC特异性生存的决定曲线显示，所有ML模型，尤其是RSF,在临床合理的风险阈值范围内治疗所有患者或不治疗患者的默认策略相比具有更高的净获益.沙普利加性移植方法显示，R0切除，肿瘤淋巴结转移分期，阳性淋巴结数量是影响5年CRC特异性生存率的重要因素。
结论：本研究显示了应用时间至事件ML预测算法来帮助预测CRC特异性生存率的潜力。RSF,GBM,考克斯时间,和N-MTLR算法可以为Cox比例风险模型提供非参数替代方法来估计CRC患者的生存概率.透明的时间到事件ML模型帮助临床医生更准确地预测这些患者的生存率，并通过启用由可解释的ML模型提供信息的个性化治疗计划来改善患者预后。
目的：本研究旨在评估ML方法对时间至事件生存数据建模的性能，并开发用于预测CRC特异性生存的透明模型。
方法：本回顾性队列研究中使用的数据集包含2012年12月28日至2019年12月27日在华西医院新诊断为CRC的患者的信息。四川大学。我们评估了6个代表性ML模型的性能，包括随机生存森林(RSF)，梯度增压机（GBM），DeepSurv,DeepHit,神经网络扩展的时间相关Cox(或Cox-Time)，和神经多任务逻辑回归（N-MTLR）预测CRC特异性生存率。采用链式方程的多重插补方法来处理变量中的缺失值。多变量分析和临床经验用于选择与CRC生存相关的重要特征。使用时间依赖性一致性指数在重复5次的分层5倍交叉验证中评估模型性能，综合Brier评分,校正曲线,和决策曲线。采用Shapley加法扩展方法计算特征重要性。
结果：本研究共纳入2157例CRC患者。在6种时间到事件ML模型中，DeepHit模型表现出最佳的辨别能力（时间依赖性一致性指数0.789，95％CI0.779-0.799），RSF模型产生了更好的校准生存估计（综合Brier评分0.096，95％CI0.094-0.099），但这些并不具有统计学意义。此外,RSF,GBM,DeepSurv,考克斯时间,和N-MTLR模型在辨别和校准方面具有与Cox比例风险模型相当的预测准确性。校准曲线显示所有ML模型均表现出良好的5年生存校准。5年CRC特异性生存的决定曲线显示，所有ML模型，尤其是RSF,在临床合理的风险阈值范围内治疗所有患者或不治疗患者的默认策略相比具有更高的净获益.沙普利加性移植方法显示，R0切除，肿瘤淋巴结转移分期，阳性淋巴结数量是影响5年CRC特异性生存率的重要因素。
结论：本研究显示了应用时间至事件ML预测算法来帮助预测CRC特异性生存率的潜力。RSF,GBM,考克斯时间,和N-MTLR算法可以为Cox比例风险模型提供非参数替代方法来估计CRC患者的生存概率.透明的时间到事件ML模型帮助临床医生更准确地预测这些患者的生存率，并通过启用由可解释的ML模型提供信息的个性化治疗计划来改善患者预后。
