Combination of intravoxel incoherent motion histogram parameters and clinical characteristics for predicting response to neoadjuvant chemoradiation in patients with locally advanced 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 locally advanced rectal cancer (LARC).

Methods: A total of 112 patients diagnosed with LARC who underwent IVIM-DWI prior to nCRT were enrolled in this study. The true diffusion coefficient (D), pseudo-diffusion coefficient (D^*), and microvascular volume fraction (f) calculated from IVIM were recorded along with the histogram parameters. The patients were classified into the pathological complete response (pCR) group and the non-pCR group according to the tumor regression grade (TRG) system. Additionally, the patients were divided 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 identify independent risk factors, including both clinical characteristics and IVIM histogram parameters. Subsequently, models for Clinical, Histogram, and Combined Clinical and Histogram were constructed using multivariable binary logistic regression analysis for the purpose of predicting pCR. The area under the receiver operating characteristic (ROC) curve (AUCs) was employed to evaluate the diagnostic performance of the three models.

Results: The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the pCR group compared with the non-pCR group (all P < 0.05). The value of D^*_ entropy was significantly lower in the pCR group compared with the non-pCR group (P < 0.05). The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the low T stage group compared with the high T stage group (all P < 0.05). The value of D^*_ entropy was significantly lower in the low T stage group compared with the high T stage group (P < 0.05). The ROC curves indicated that the Combined Clinical and Histogram model exhibited the best diagnostic performance in predicting the pCR patients with AUCs, sensitivity, specificity, and accuracy of 0.916, 83.33%, 85.23%, and 84.82%.

Conclusions: The histogram parameters derived from IVIM have the potential to identify patients who have achieved pCR. Moreover, the combination of IVIM histogram parameters and clinical characteristics enhanced the diagnostic performance of IVIM histogram parameters.