MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma.

Abstract

Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient. To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model. Retrospective. A total of 286 LANPC patients were assigned to training (N= 200, 43.8± 10.9 years, 152 male) and testing (N= 86, 43.5± 11.3 years, 57 male) cohorts. T2 -weighted imaging, contrast enhanced-T1 -weighted imaging using fast spin echo sequences at 1.5T scanner. Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single-factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Modelclinic ), radiomics features (Modelradiomics ), and clinical factors + radiomics signatures using logistic (Modelcombined ), and BPNN (ModelBPNN ) methods were established, and model performances were compared. Student's t-test, Mann-Whitney U-test, and Chi-square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P< 0.05 was considered statistical significance. Three significant clinical factors: Epstein-Barr virus-DNA (odds ratio [OR]=1.748; 95% confidence interval [CI], 0.969-3.171), sex (OR=2.883; 95% CI, 1.364-6.745), and T stage (OR=1.853; 95% CI, 1.201-3.052) were identified via univariate and multivariate logistic models. Twenty-four radiomics features were associated with treatment response. ModelBPNN demonstrated the highest performance among Modelcombined , Modelradiomics , and Modelclinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695). A machine-learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC. 3 TECHNICAL EFFICACY: Stage 2.