Dong Xuchen, Gui Haohuai, Quan Kai, Li Zongze, Xiao Ying, Zhou Jiaxi, Zhao Yuchuan, Wang Dongdong, Liu Mingjian, Duan Haojing, Yang Shaoxuan, Lin Xiaolei, Dong Jun, Wang Lin, Ma Yu, Zhu Wei
1Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai.
5Shanghai Clinical Medical Center of Neurosurgery, Shanghai.
Neurosurg Focus. 2025 Jul 1;59(1):E7. doi: 10.3171/2025.4.FOCUS24778.
Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to construct a BSCM surgery outcome prediction model based on clinical characteristics and T2-weighted MRI-based radiomics.
Two separate cohorts of patients undergoing BSCM resection were included as discovery and validation sets. Patient characteristics and imaging data were analyzed. An unfavorable outcome was defined as a modified Rankin Scale score > 2 at the 12-month follow-up. Image features were extracted from regions of interest within lesions and adjacent brainstem. A nomogram was constructed using the risk score from the optimal model.
The discovery and validation sets comprised 218 and 49 patients, respectively (mean age 40 ± 14 years, 127 females); 63 patients in the discovery set and 35 in the validation set had an unfavorable outcome. The eXtreme Gradient Boosting imaging model with selected radiomics features achieved the best performance (area under the receiver operating characteristic curve [AUC] 0.82). Patients were stratified into high- and low-risk groups based on risk scores computed from this model (optimal cutoff 0.37). The final integrative multimodal prognostic model attained an AUC of 0.90, surpassing both the imaging and clinical models alone.
Inclusion of BSCM and brainstem subregion imaging data in machine learning models yielded significant predictive capability for unfavorable postoperative outcomes. The integration of specific clinical features enhanced prediction accuracy.
鉴于脑干海绵状血管畸形（BSCMs）切除可终止出血，但存在神经功能缺损的高风险，有必要开发并验证一个预测手术结果的模型。本研究旨在构建一个基于临床特征和基于T2加权磁共振成像（MRI）的影像组学的BSCM手术结果预测模型。
纳入两个独立队列的接受BSCM切除的患者作为发现集和验证集。分析患者特征和影像数据。不良结果定义为在12个月随访时改良Rankin量表评分>2。从病变内和相邻脑干的感兴趣区域提取影像特征。使用最优模型的风险评分构建列线图。
发现集和验证集分别包含218例和49例患者（平均年龄40±14岁，女性127例）；发现集中63例患者和验证集中35例患者有不良结果。具有选定影像组学特征的极限梯度提升影像模型表现最佳（受试者操作特征曲线下面积[AUC]为0.82）。根据该模型计算的风险评分将患者分为高风险组和低风险组（最佳截断值为0.37）。最终的综合多模态预后模型的AUC为0.90，超过了单独的影像模型和临床模型。
将BSCM和脑干亚区域影像数据纳入机器学习模型对不良术后结果具有显著的预测能力。特定临床特征的整合提高了预测准确性。
