Chai Xinyi, Yang Wei, Cai Yingjie, Peng Xiaojiao, Qiu Xuemeng, Ling Miao, Yang Ping, Chen Jiashu, Zhang Hong, Ma Wenping, Ni Xin, Ge Ming
Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100024, China.
Children (Basel). 2025 May 23;12(6):667. doi: 10.3390/children12060667.
To develop and validate a composite model that combines lesion-symptom mapping (LSM), radiomic information, and clinical factors for predicting cerebellar mutism syndrome in pediatric patients suffering from posterior fossa tumors. A retrospective analysis was conducted on a cohort of 247 (training set, = 174; validation set, = 73) pediatric patients diagnosed with posterior fossa tumors who underwent surgery at Beijing Children's Hospital. Presurgical MRIs were used to extract the radiomics features and voxel distribution features. Clinical factors were derived from the medical records. Group comparison was used to identify the clinical risk factors of CMS. Combining location weight, radiomic features from tumor area and the significant intersection area, and clinical variables, hybrid models were developed and validated using multiple machine learning models. The mean age of the cohort was 4.88 [2.89, 7.78] years, with 143 males and 104 females. Among them, 73 (29.6%) patients developed CMS. Gender, location, weight, and five radiomic features (three in the tumor mask area and two in the intersection area) were selected to build the model. The four models, KNN model, GBM model, RF model, and LR model, achieved high predictive performance, with AUCs of 0.84, 0.83, 0.81, and 0.87, respectively. CMS can be predicted using MRI features and clinical factors. The combination of radiomics and tumoral location weight could improve the prediction of CMS.
开发并验证一种综合模型，该模型结合病变-症状映射（LSM）、放射组学信息和临床因素，用于预测患有后颅窝肿瘤的儿科患者的小脑缄默综合征。对在北京儿童医院接受手术的247例（训练集，n = 174；验证集，n = 73）被诊断为后颅窝肿瘤的儿科患者队列进行了回顾性分析。术前MRI用于提取放射组学特征和体素分布特征。临床因素来自病历。采用组间比较来确定CMS的临床危险因素。结合位置权重、肿瘤区域和显著交集区域的放射组学特征以及临床变量，使用多种机器学习模型开发并验证了混合模型。该队列的平均年龄为4.88[2.89, 7.78]岁，其中男性143例，女性104例。其中，73例（29.6%）患者发生了CMS。选择性别、位置、权重和五个放射组学特征（肿瘤掩码区域中的三个和交集区域中的两个）来构建模型。KNN模型、GBM模型、RF模型和LR模型这四个模型具有较高的预测性能，AUC分别为0.84、0.83、0.81和0.87。可以使用MRI特征和临床因素来预测CMS。放射组学与肿瘤位置权重的结合可以提高对CMS的预测。
