A Clinical-Radiomics Nomogram Based on Pretreatment Magnetic Resonance Imaging Predicting Tumor Residual at the End of Radiotherapy in Patients With Nasopharyngeal Carcinoma.

BACKGROUND: The TNM staging system is a main tool for treatment stratification and prognosis prediction of nasopharyngeal carcinoma (NPC). However, patients with identical clinical stages often show different tumor regression patterns. PURPOSE: This study is aimed at constructing a nomogram model integrating MRI and clinical variables to predict primary tumor residual status at the completion of radiotherapy for NPC cases. STUDY TYPE: The present study is a retrospective design. POPULATION: Two hundred NPC patients who accepted radical radiotherapy in the present hospital from December 2017 to March 2023 were systematically reviewed. The selected 200 NPC cases were divided into the training cohort (n = 160) and validation cohort (n = 40). FIELD STRENGTH/SEQUENCE: 1.5T MRI scan, contrast-enhanced T1-weighted (CE-T1WI) and T2-weighted (T2WI) sequences. ASSESSMENT: For the training cohort, the LASSO with logistic regression was conducted to choose important features and construct a radiomics model. The clinical variables of patients were selected on the basis of statistical analysis and previous research. The nomogram was developed by combining the clinical factors with the radiomics features. STATISTICAL TESTS: Statistical analyses were conducted using R software for correlation analysis, chi-squared test, multivariable regression analysis, Mann-Whitney U test, ROC curve analysis, and DeLong test. Significant level was p < 0.05. RESULTS: Up to 109 radiomics characteristics were extracted, from which seven stable characteristics were selected to generate the Rad-score. According to the regression result, T stage can be regarded as an independent predictor of primary tumor residual status. A nomogram combining the Rad-score and clinical variables was established and proved, yielding an AUC of 0.836 (95% CI: 0.766-0.889, p < 0.001) in the training set and 0.843 (p < 0.001) in the validation set, respectively. The DeLong test indicates that the nomogram model is superior to both the individual clinical variables and the Rad-score alone. The calibration curve and decision curve analyses proved the validity and clinical utility of the proposed model. DATA CONCLUSION: A clinical-radiomics nomogram has good efficacy in predicting primary tumor residual after radiotherapy, providing individual treatment strategies for NPC cases.