Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China.
Department of Library, Shengjing Hospital of China Medical University, Shenyang, China,
Neuroendocrinology. 2024;114(8):733-748. doi: 10.1159/000539187. Epub 2024 May 6.
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.
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.
A total of 43,444 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 years 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.
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.
用于预测胃肠道神经内分泌肿瘤（GINENs）患者的个体化预后的校准良好的模型有限。本研究旨在开发和验证一种机器学习模型，以预测 GINENs 患者的生存情况。
比较了斜向随机生存森林（ORSF）模型、Cox 比例风险风险模型、具有最小绝对收缩和选择算子惩罚的 Cox 模型、CoxBoost、生存梯度提升机、极端梯度提升生存回归、DeepHit、DeepSurv、DNNSurv、逻辑风险模型和 PC 风险模型。我们进一步调整了最佳性能的 ORSF 的超参数并选择了变量。然后，对最终的 ORSF 模型进行了验证。
共纳入 43444 例 GINENs 患者。中位（四分位间距）生存时间为 53（19-102）个月。ORSF 模型表现最佳，其中年龄、组织学、M 分期、肿瘤大小、原发肿瘤部位、性别、肿瘤数量、手术、淋巴结切除、N 分期、种族和分级被列为重要变量。然而，化疗和放疗对 ORSF 模型并非必要。ORSF 模型的整体 C 指数为 0.86（95%置信区间，0.85-0.87）。1、3、5 和 10 年时的接收器操作曲线下面积分别为 0.91、0.89、0.87 和 0.80。决策曲线分析表明，ORSF 模型比美国癌症联合委员会分期具有更好的临床实用性。给出了一个诺模图和一个在线工具。
机器学习 ORSF 模型可以精确预测 GINENs 患者的生存情况，能够识别出死亡风险较高的患者，并可能指导临床实践。
