Predictive performance of MRI and CT radiomics in predicting the response to induction chemotherapy in nasopharyngeal carcinoma: a network meta-analysis

Objectives To evaluate the accuracy of different radiomics methods in predicting the response of nasopharyngeal carcinoma (NPC) to induction chemotherapy (IC). Methods A systematic search was conducted in PubMed, Embase, Web of Science, and Cochrane Library. Radiomics studies utilizing CT and MRI were included in this network meta-analysis. The quality of the studies was appraised via the PROBAST, RQS, and IBSI guidelines. The sensitivity, specificity, and accuracy of different radiomics models were analyzed. Results Ten eligible studies involving 1550 subjects were included. The pooled sensitivity and specificity of the radiomics models were 0.86 (95% CI: 0.78-0.91) and 0.69 (95% CI: 0.62-0.75), respectively. The AUC based on the SROC curve was 0.83 (95% CI: 0.70-0.91). The predictive performance of each model was rated using SUCRA values. The MRI-based support vector machine radiomics model had the highest specificity, and accuracy, at 80.7% and 73.2%, respectively. The MRI-based SVM radiomics combined with clinical features model had the highest sensitivity (82.0%). Among the CT methods, the deep learning (DL)-based convolutional neural network model had the highest sensitivity, and accuracy, at 51.0% and 44.9%, respectively. The PROBAST showed that 7 studies were at risk for bias. Conclusion This study synthesized existing evidence to confirm that radiomics serves as a viable exploratory tool for predicting IC efficacy in NPC. MRI-based nonlinear models and clinical-radiomics fusion models exhibit considerable promise, whereas clinical translation necessitates three critical steps: (1) standardized protocols following IBSI/METRICS/RQS guidelines; (2) prospective multicenter validation; and (3) investigating tumor microenvironment mechanisms. These measures will facilitate the transition of radiomics from technical exploration to clinical utility. Systematic Review Registration https://www.crd.york.ac.uk/prospero/ , identifier CRD42024509331.