Supplementary Material for: Development and Validation of a Novel Machine Learning Model to predict the Survival of Patients with Gastrointestinal Neuroendocrine Neoplasms

Introduction: Well-calibrated models for personalized prognostication of patients with gastrointestinal neuroendocrine neoplasms (GINENs) are limited. This study aimed to develop and validate a machine-learning model to predict the survival of patients with GINENs. Methods: oblique random survival forest (ORSF) model, Cox proportional hazard risk model, Cox model with least absolute shrinkage and selection operator penalization, CoxBoost, Survival Gradient Boosting Machine, Extreme Gradient Boosting survival regression, DeepHit, DeepSurv, DNNSurv, Logistic-Hazard model, and PC-Hazard model were compared. We further tuned hyperparameters and selected variables for the best-performing ORSF. Then, the final ORSF model was validated. Results: 43444 patients with GINENs were included. The median (interquartile range) survival time was 53 (19-102) months. The ORSF model performed best, in which age, histology, M stage, tumor size, primary tumor site, sex, tumor number, surgery, lymph nodes removed, N stage, race, and grade were ranked as important variables. However, chemotherapy and radiotherapy were not necessary for the ORSF model. The ORSF model had an overall C-index of 0.86 (95% confidence interval, 0.85-0.87). The area under the receiver operation curves at 1-, 3-, 5-, and 10-year were 0.91, 0.89, 0.87, and 0.80, respectively. The decision curve analysis showed superior clinical usefulness of the ORSF model than the American Joint Committee on Cancer Stage. A nomogram and an online tool were given. Conclusion: The machine-learning ORSF model could precisely predict the survival of patients with GINENs, with the ability to identify patients at high risk for death and probably guide clinical practice.

引言：目前用于胃肠道神经内分泌肿瘤（gastrointestinal neuroendocrine neoplasms, GINENs）患者个性化预后的校准良好的模型较为匮乏。本研究旨在开发并验证一款机器学习模型，用于预测GINENs患者的生存情况。
方法：本研究对比了倾斜随机生存森林（oblique random survival forest, ORSF）模型、Cox比例风险模型、带最小绝对收缩与选择算子惩罚的Cox模型、CoxBoost、生存梯度提升机、极限梯度提升生存回归、DeepHit、DeepSurv、DNNSurv、Logistic-Hazard模型以及PC-Hazard模型的性能。我们进一步对性能最优的ORSF模型进行超参数调优与变量筛选，随后对最终的ORSF模型开展验证。
结果：本研究共纳入43444例GINENs患者，患者的中位（四分位距）生存时间为53（19~102）个月。结果显示，ORSF模型表现最优，其中年龄、组织学类型、M分期、肿瘤大小、原发肿瘤部位、性别、肿瘤数量、手术治疗、清扫淋巴结数目、N分期、种族以及肿瘤分级被列为重要预测变量，而化疗与放疗未被该模型纳入必要影响因素。该ORSF模型的整体C指数为0.86（95%置信区间：0.85~0.87），1年、3年、5年及10年受试者工作特征曲线下面积分别为0.91、0.89、0.87与0.80。决策曲线分析表明，相较于美国癌症联合委员会分期系统，ORSF模型具有更优异的临床实用性。本研究同时构建了列线图并提供了在线工具。
结论：本研究开发的机器学习ORSF模型可精准预测GINENs患者的生存情况，能够识别出死亡高风险人群，有望指导临床实践。