Development of a neoadjuvant chemotherapy efficacy prediction model for nasopharyngeal carcinoma integrating magnetic resonance radiomics and pathomics: a multi-center retrospective study

作者：Yiren Wang, Huaiwen Zhang, Huan Wang, Yiheng Hu, Zhongjian Wen, Hairui Deng, Delong Huang, L. Xiang, Yun Zheng, Lu Yang, Lei Su, Yunfei Li, Fang Liu, Peng Wang, Shengmin Guo, Haowen Pang, Ping Zhou · 发表于：BMC Cancer · 年份：2024 · DOI：10.1186/s12885-024-13235-0 · 被引用次数：4 · 研究领域：Head and Neck Cancer Studies、Radiomics and Machine Learning in Medical Imaging、Esophageal Cancer Research and Treatment

OBJECTIVE: This study aimed to develop and validate a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NACT) in nasopharyngeal carcinoma (NPC) by integrating radiomics and pathomics features using a particle swarm optimization-supported support vector machine (PSO-SVM). METHODS: A retrospective multi-center study was conducted, which included 389 NPC patients who received NACT from three institutions. Radiomics features were extracted from magnetic resonance imaging scans, while pathomics features were derived from histopathological images. A total of 2,667 radiomics features and 254 pathomics features were initially extracted. Feature selection involved intra-class correlation coefficient evaluation, Mann-Whitney U test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression. The PSO-SVM model was constructed and validated using 10-fold cross-validation on the training set and further evaluated using an external validation set. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve, calibration curves, and decision curve analysis. RESULTS: Eight significant predictive features (five radiomics and three pathomics) were identified. The PSO-SVM radiopathomics model achieved superior performance compared to models based solely on radiomics or pathomics features. The AUCs for the PSO-SVM radiopathomics model were 0.917 (95% CI: 0.887-0.948) in internal valida...