Combination of intravoxel incoherent motion histogram parameters and clinical characteristics for predicting response to neoadjuvant chemoradiation in Rectal Cancer

Abstract

Objective To explore the value of histogram parameters derived from intravoxel incoherent motion (IVIM) for predicting response to neoadjuvant chemoradiation (nCRT) in patients with rectal cancer. Methods 112 patients diagnosed with rectal cancer who underwent IVIM-DWI before nCRT were enrolled in this study, and true diffusion coefficient (D), pseudo-diffusion coefficient (D^*), and microvascular volume fraction (f) calculated from IVIM, together with the histogram parameters were recorded. The patients were divided into the pathological complete response (pCR) group and the non-pCR group according to the tumor regression grade (TRG) system. We also divided the patients into low T stage (yp T0-2) and high T stage (ypT3-4) according to the pathologic T stage (ypT stage). Univariate logistic regression analysis was implemented to select independent risk factors, including clinical characteristics and IVIM histogram parameters, and the models for Clinical, Histogram, and Combined Clinical and Histogram were generated respectively by using multivariable binary logistic regression analysis for predicting pCR. The area under the Receiver operating characteristic (ROC) curve (AUCs) were used to compare the diagnostic performance among the three models. Results The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the pCR group (n = 24) compared with the non-pCR group. The value of D^*_ entropy was significantly lower in the pCR group compared with the non-pCR group. The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the low T stage group (n=37) compared with the high T stage group. The value of D^*_ entropy was significantly lower in the low T stage group compared with the high T stage group (all p < 0.05). ROC curves demonstrated that the Combined Clinical and Histogram model had the best diagnostic performance in predicting the pCR patients with optimal AUCs, sensitivity, specificity, and accuracy (0.916, 83.33%, 85.23%, and 84.82%, respectively). Conclusions IVIM histogram parameters which combined with clinical characteristics showed promising prospects in predicting the pCR patients before surgery.