Wang Dongdong, Wang Xinfeng, Yang Xin
Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Front Oncol. 2025 May 21;15:1586262. doi: 10.3389/fonc.2025.1586262. eCollection 2025.
This study aims to externally validate the performance of the Oncotype DX (ODX) breast cancer (BC) recurrence score nomogram in predicting adjuvant chemotherapy (ACT) for BC after surgery and subsequently develop a machine learning-based model to predict postoperative overall survival (OS) and guide ACT, demonstrating superior comprehensive performance.
This analysis leveraged data from the SEER database spanning 2010-2020, alongside a BC cohort from the Beijing Hospital (BJH). Machine learning methods were applied for predictor selection by wrapper methods and the development of the predictive model. The optimal model was determined using the concordance index (C-index), time-dependent calibration curves, time dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). The benefit analysis of ACT was primarily conducted using Kaplan-Meier survival analysis.
The ODX nomogram performed poorly in predicting ACT benefit in both the SEER cohort and the BJH cohort. Subsequently, we employed ten machine learning methods to develop ten prognostic models. The Accelerated oblique random survival forest model (AORSFM), exhibiting the highest prediction performance, was selected. The C-index for AORSFM is 0.799 (95% CI 0.779-0.823) in the SEER cohort and 0.793 (95% CI 0.687-0.934) in the BJH cohort. Furthermore, time-dependent calibration curves, time-dependent ROC analysis, and DCA indicate that the AORSFM demonstrates good calibration, predictive accuracy, and clinical net benefit. A publicly accessible web tool was developed for the AORSFM. Notably, the new staging system based on AORSFM can provide guidance for postoperative ACT in such patients.
The AORSF has the potential to identify postoperative OS and guide ACT in patients with BC. This can assist clinicians in assessing the severity of the disease, facilitating patient follow-up, and aiding in the formulation of adjuvant treatment strategies.
本研究旨在外部验证Oncotype DX（ODX）乳腺癌（BC）复发评分列线图在预测BC术后辅助化疗（ACT）方面的性能，并随后开发一种基于机器学习的模型来预测术后总生存期（OS）并指导ACT，以展示卓越的综合性能。
本分析利用了2010 - 2020年SEER数据库的数据以及北京医院（BJH）的一个BC队列。通过包装法应用机器学习方法进行预测因子选择和预测模型的开发。使用一致性指数（C指数）、时间依赖性校准曲线、时间依赖性受试者工作特征（ROC）曲线和决策曲线分析（DCA）来确定最优模型。ACT的获益分析主要使用Kaplan - Meier生存分析进行。
ODX列线图在预测SEER队列和BJH队列中的ACT获益方面表现不佳。随后，我们采用了十种机器学习方法来开发十个预后模型。选择了预测性能最高的加速斜向随机生存森林模型（AORSFM）。AORSFM在SEER队列中的C指数为0.799（95%CI 0.779 - 0.823），在BJH队列中的C指数为0.793（95%CI 0.687 - 0.934）。此外，时间依赖性校准曲线、时间依赖性ROC分析和DCA表明AORSFM具有良好的校准、预测准确性和临床净获益。为AORSFM开发了一个可公开访问的网络工具。值得注意的是，基于AORSFM的新分期系统可以为这类患者的术后ACT提供指导。
AORSF有潜力识别BC患者的术后OS并指导ACT。这可以帮助临床医生评估疾病的严重程度，便于患者随访，并有助于制定辅助治疗策略。
