Shi Yuhui, Liu Xianguo
Department of Oncology, 363 Hospital, Chengdu, China.
Front Oncol. 2025 Apr 16;15:1585125. doi: 10.3389/fonc.2025.1585125. eCollection 2025.
BACKGROUND/AIM: This study develops a machine learning-based predictive model to evaluate the survival outcomes of elderly patients with unresectable hepatocellular carcinoma (HCC) undergoing radiotherapy.
The 2377 patients from SEER database were divided into training and internal validation cohorts. Additionally, 99 patients from our hospital were used for an external validation cohort. In the training cohort, 101 machine learning-based radiomics models were developed, and the optimal model's performance was subsequently evaluated in both the internal and external validation cohorts.
The StepCox + GBM model demonstrated the highest C-index of 0.7 in the training cohort. The model was further evaluated using area under the receiver operating characteristic (AUC-ROC) curves, with AUC values ranging from 0.736 to 0.783, indicating strong predictive performance. Furthermore, the calibration curve and decision curves confirmed that the model had good predictive performance.
The StepCox + GBM model could help optimize the use of radiotherapy for elderly HCC patients, improving survival outcomes and guiding personalized treatment strategies.
背景/目的：本研究开发了一种基于机器学习的预测模型，以评估接受放疗的不可切除肝细胞癌（HCC）老年患者的生存结局。
将来自监测、流行病学和最终结果（SEER）数据库的2377例患者分为训练队列和内部验证队列。此外，将我院的99例患者用作外部验证队列。在训练队列中，开发了101个基于机器学习的放射组学模型，随后在内部和外部验证队列中评估最佳模型的性能。
在训练队列中，StepCox +梯度提升机（GBM）模型显示出最高的C指数，为0.7。使用受试者工作特征曲线下面积（AUC-ROC）对该模型进行进一步评估，AUC值范围为0.736至0.783，表明具有较强的预测性能。此外，校准曲线和决策曲线证实该模型具有良好的预测性能。
StepCox + GBM模型有助于优化老年HCC患者放疗的使用，改善生存结局并指导个性化治疗策略。
