The value of multi-parametric MRI radiomics in the prediction of neoadjuvant therapy for rectal mucinous adenocarcinoma / 中华放射学杂志

ABSTRACT

Objective:To investigate the application value of baseline MRI multi-parametric imaging radiomics in prediction of neoadjuvant chemoradiotherapy (NCR) efficacy of rectal mucinous adenocarcinoma (RMAC).Methods:Retrospective analysis was performed in the Sixth Affiliated Hospital of Sun Yat-sen University from August 2012 to October 2018. A total of 79 patients were included in this study, including 52 males and 27 females, aged 20-78 years (median age 52 years). According to the classification criteria of pathological regression, all patients were divided into NCR responsiveness group ( n=31) and nonresponsiveness group ( n=48). And 701 imaging features of T 2WI, diffusion weighted imaging (DWI) and enhanced T 1WI images of baseline MRI were extracted, and feature subsets were selected by repeatability analysis and feature dimensionality reduction to construct the radiomics prediction model. The tumor features from baseline MRI between the NCR responsiveness group and the nonresponsiveness group were compared, and the features of P<0.05 were combined with the radiomics to construct a model. Using pathology as the gold standard, the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the prediction model, and the area under the curve (AUC), 95% confidence interval, sensitivity and specificity were calculated, and the DeLong test was used to compare the diagnostic efficacy of different prediction models. Results:By comparing the conventional tumor imaging characteristics of the NCR responsiveness group and the nonresponsiveness group, the differences in lymph node stage and mucinous nodule status between the two groups were statistically significant (χ2 =6.040, 5.870, P<0.05). The AUC of ROC curves based on T 2WI, DWI, and enhanced T 1WI radiomics were 0.816, 0.821, and 0.819, respectively, which were higher than those of conventional tumor characteristics (lymph node staging, mucinous nodule status) (AUC=0.607), and the differences were statistically significant ( Z=-2.391, -2.580 and -2.717, P<0.05). Among the joint prediction models of T 2WI, DWI and contrast-enhanced T 1WI radiomics and conventional tumor features, the DWI combined model had the largest AUC (0.843), and there was no statistically significant difference between the three combined models (all P>0.05). Conclusion:The baseline T 2WI, DWI, and contrast-enhanced T 1WI radiomics model can be used to predict the NCR efficacy of RMAC, which is better than the predictive efficacy of conventional features, and the combination with conventional features can further improve the predictive efficacy.