Abstract

Tumor heterogeneity can be assessed by texture analysis (TA). TA has been applied using diffusion-weighted imaging and apparent diffusion coefficient maps to predict pathological responses to preoperative chemoradiation therapy (CRT) in patients with locally advanced rectal cancer (LARC). To evaluate the texture parameters obtained from K trans maps derived from dynamic contrast-enhanced (DCE)-MRI for predicting pathological responses to preoperative CRT for LARCs. Retrospective. Altogether, 83 patients (26 women, 57 men) with rectal cancer met the inclusion criteria. 3.0T/T1 -weighted DCE-MRI sequence. After CRT, each tumor was assessed by a pathologist who assigned a tumor regression grade (TRG), thereby identifying pathologically complete responders (pCR; TRG 1) and good responders (GR; TRG1 + TRG2). TA was then applied to the DCE-MRI K trans maps. The K trans value, several TA parameters, and tumor volumes were calculated. The Shapiro-Wilk test was used to verify that the data had normal distribution. Results of parameters measured before and after CRT were compared using paired-sample t-tests. Value changes of each parameter in the combined pCR/GR group were compared using independent sample t-tests. Receiver operating characteristic curves and areas under the curve (AUC) were calculated to assess the diagnostic performance of each parameter related to CRT effectiveness. There were 15 pCR (16.9%) and 21 GR (25.3%) patients. Tumor volume, mean K trans , entropy, and correlation decreased and energy values increased significantly in these groups compared with those of the non-PCR and non-GR groups. ΔCorrelation (Δcorrelation = postcorrelation - precorrelation) was found to be a valuable parameter for identifying pCR/GR patients (AUC 0.895, sensitivity 86.7%, specificity 81.8%). TA parameters from the DCE-MRI K trans map can predict the efficacy of CRT for treating LARCs. Also, Δcorrelation may be useful for identifying patients who will be responsive to CRT. 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:885-893.

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