Radiomic analysis based on machine learning of multi-sequences MR to assess early treatment response in locally advanced nasopharyngeal carcinoma

ObjectiveThe prediction of early response in locally advanced nasopharyngeal carcinoma (LA-NPC) after concurrent chemoradiotherapy (CCRT) is important for determining the need for timely consolidation therapy. We developed a radiomic analysis of multi-sequences MR based on machine learning (ML) to assess early response in LA-NPC after CCRT.MethodsThis study retrospectively enrolled 104 LA-NPC patients, randomly divided into training (70%) and test (30%) cohorts. Radiomic features were extracted from five MR sequences (T1, T1C, T2, DWI, and ADC). Feature selection was performed using Pearson's correlation coefficient and LASSO regression to reduce redundancy. ML algorithms were compared to develop models, with suboptimal sequences excluded from the multi-sequence MR fusion model. A combined model integrating the fusion and clinical model was developed using logistic regression, and its diagnostic effectiveness was evaluated using receiver operating characteristic (ROC) analysis.ResultsIn the mono-sequence MR analysis, T1 demonstrated the lowest discriminative capacity (AUC = 0.505), followed by T2 (AUC = 0.738). Consequently, we developed a fusion model incorporating ADC, DWI, and T1C features while excluding T1 and T2. In the test cohort, the combined model outperformed both the clinical (AUC = 0.852) and fusion (AUC = 0.886) models, achieving superior effectiveness (AUC = 0.900). Shapley Additive Explanations (SHAP) analysis identified lbp_GrayLevelVariance_ADC as the most influential predictive feature.ConclusionsA combined model, which merges clinical and multi-sequences MR radiomics model, showed good performance for predicting early response of LA-NPC after CCRT.