Multi-parametric MRI radiomics for predicting response to neoadjuvant therapy in patients with locally advanced rectal cancer.

Abstract

This study aims to evaluate the application value of multi-parametric magnetic resonance imaging (MRI) radiomics in predicting the response of patients with locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy(nCRT), aiming to provide non-invasive biomarkers for clinical decision-making in personalized treatment. A retrospective analysis was conducted on the clinical data and imaging records of patients with LARC who received nCRT and total mesorectal excision (TME) in two medical centers from 2017 to 2023. The patients were divided into a training group and a test group in a 7:3 ratio. Through radiomics analysis, radiomics features of tumor volume and mesorectal fat at baseline, before and after neoadjuvant therapy were extracted. Radiomics models based on single sequences (T2WI, DWI) and multi-sequence fusion were constructed, and the logistic regression classifier model was used to evaluate the prediction performance. A total of 82 patients were included, with 30 in the good response group and 52 in the poor response group. Through the selection of radiomics features, radiomics models based on baseline MRI of tumor volume, mesorectal fat, and differences before and after treatment (Delta) were constructed. The area under the receiver operating characteristic curve (AUC) of the multi-parametric radiomics fusion model in the training and test groups was 0.852 and 0.848, respectively, showing high prediction performance and good calibration. This study demonstrates that the multi-parametric MRI radiomics model can effectively predict the response of patients with locally advanced rectal cancer to neoadjuvant chemoradiotherapy. Especially, the fusion model provides high accuracy and good calibration. This result is conducive to the formulation of personalized treatment plans and optimization of treatment strategies.