Application Value of Magnetic Resonance Radiomics and Clinical Nomograms in Evaluating the Sensitivity of Neoadjuvant Chemotherapy for Nasopharyngeal Carcinoma

Objective To predict the sensitivity of nasopharyngeal carcinoma (NPC) to neoadjuvant chemotherapy (NACT) based on magnetic resonance (MR) radiomics and clinical nomograms prior to NACT. Materials and methods From January 2014 to July 2015, 284 consecutive patients with pathologically confirmed NPC underwent 3.0 T MR imaging (MRI) before initiating NACT. The patients' data were randomly assigned to a training set (n = 200) or a test set (n = 84) at a ratio of 7:3. The clinical data included sex, tumor (T) stage, lymph node (N) stage, American Joint Committee on Cancer (AJCC) stage, and the plasma concentration of Epstein-Barr virus (EBV) DNA. The regions of interest (ROI) were manually segmented on the axial T2-weighted imaging (T2WI) and enhanced T1-weighted imaging (T1WI) sequences using ITK-SNAP software. The radiomics data were post-processed using AK software. Moreover, the Maximum Relevance Minimum Redundancy (mRMR) algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) were adopted for dimensionality reduction to screen for the features that best predicted the treatment efficacy, and clinical risk factors were used in combination with radiomics scores (Rad-scores) to construct the clinical radiomics-based nomogram. DeLong's test was utilized to compare the area under the curve (AUC) values of the clinical radiomics-based nomogram, radiomics model, and clinical nomogram. Decision curve analysis (DCA) was employed to evaluate each model's net benefit. Results The clinical nomogram was constructed based on data from patients who were randomly assigned according to T2WI and enhanced T1WI sequences. In the training set, the T2WI sequence-based clinical radiomics nomogram and the radiomics model outperformed the clinical nomogram in predicting the NACT efficacy (AUC, 0.81 vs. 0.60, p = 0.001279 and 0.76 vs. 0.60, p = 0.03026). These findings were well-verified in the test set. The enhanced T1WI sequence-based clinical radiomics nomogram exhibited better performance in predicting treatment efficacy than the clinical nomogram (AUC, 0.79 vs. 0.62, respectively; p = 0.0000834). The DCA revealed that the T2WI and clinical radiomics-based nomograms resulted in a net benefit in predicting the NACT efficacy. Conclusion The clinical radiomics-based nomogram improved the prediction of NACT efficacy, with the T2WI sequence-based clinical radiomics achieving the best effect.