手术切除是大面积或有症状脑转移 (BM) 患者的标准治疗方法。尽管辅助立体定向放射治疗后局部控制有所改善，但局部失败（LF）的风险仍然存在。因此，我们的目的是开发和外部验证一种基于治疗前放射组学的预测工具，以识别高 LF 风险的患者。数据收集自脑转移瘤切除腔立体定向放疗的多中心分析 (AURORA) 回顾性研究（训练队列：来自两个中心的 253 名患者；外部测试队列：来自五个中心的 99 名患者）。从对比增强 BM（T1-CE MRI 序列）和周围水肿（FLAIR 序列）中提取放射组学特征。比较了放射组学和临床特征的不同组合。最终模型在整个训练队列上进行训练，使用先前通过内部 5 倍交叉验证确定的最佳参数集进行训练，并在外部测试集上进行测试。外部测试中的最佳性能是通过训练的弹性网络回归模型实现的放射组学和临床特征的结合，一致性指数 (CI) 为 0.77，优于任何临床模型（最佳 CI：0.70）。该模型在 Kaplan-Meier 分析中根据 LF 风险有效地对患者进行了分层（p < 0.001），并证明了增量的净临床效益。 24 个月时，我们发现低风险组和高风险组的 LF 发生率分别为 9% 和 74%。临床和放射组学特征的结合比单独的任何临床特征组更能预测无 LF。 LF 高风险患者可能会受益于更严格的随访常规或强化治疗。© 作者 2024。由牛津大学出版社代表神经肿瘤学会出版。

Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk.Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of Brain Metastases (AURORA) retrospective study (training cohort: 253 patients from two centers; external test cohort: 99 patients from five centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set.The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (p < 0.001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively.A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.