Multimodal PET/CT–MRI Radiomics and Deep Learning Fusion for Individualized Prognostic Prediction in Nasopharyngeal Carcinoma

作者：Yukun Cai, Da Pan, Zhang Jingjing, Zhaoqing Chen, Kun Chen, Changyu Sun, Yuqi Su, Jing Li, Heqing Yi · 发表于：International Journal of Clinical Practice · 年份：2026 · DOI：10.1155/ijcp/1364442 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Head and Neck Cancer Studies、MRI in cancer diagnosis

Objectives Nasopharyngeal carcinoma shows considerable biological heterogeneity that leads to wide variations in clinical outcomes. Anatomy‐based staging and single‐modality imaging cannot fully represent the complexity of tumor phenotype. This study aimed to develop and validate a prognostic model for progression‐free survival by integrating multimodal positron emission tomography, computed tomography, magnetic resonance imaging, quantitative radiomics, deep learning representations, and clinical variables. Methods A total of 261 patients with locoregionally advanced disease were retrospectively included. Radiomics and deep learning features were extracted from pretreatment positron emission tomography/computed tomography and T1‐weighted magnetic resonance imaging. Fourteen survival models were constructed using Cox proportional hazards regression. Model performance was assessed through concordance index, time‐dependent receiver operating characteristic analysis, Brier score, and calibration. The Friedman–Nemenyi procedure compared overall performance across models, and SHapley Additive Explanation identified key prognostic contributors. Results Multimodal fusion models outperformed clinical‐only and single‐modality models. The fully integrated model achieved the highest discrimination and stable performance across multiple follow‐up intervals, with consistently strong calibration. Ranking analysis showed that deep‐feature‐dominant models performed worst, radiomics‐based mod...