Predicting Efficacy of Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer Using Transrectal Contrast-Enhanced Ultrasound-Based Radiomics Model.

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Abstract

Predicting tumor regression grade (TRG) after neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC) preoperatively accurately is crucial for providing individualized treatment plans. This study aims to develop transrectal contrast-enhanced ultrasound-based (TR-CEUS) radiomics models for predicting TRG. A total of 190 LARC patients undergoing NCRT and subsequent total mesorectal excision were categorized into good and poor response groups based on pathological TRG. TR-CEUS examinations were conducted before and after NCRT. Machine learning (ML) models for predicting TRG were developed by employing pre- and post-NCRT TR-CEUS image series, based on seven classifiers, including random forest (RF), multi-layer perceptron (MLP) and so on. The predictive performance of models was evaluated using receiver operating characteristic curve analysis and Delong test. A total of 1525 TR-CEUS images were included for analysis, and 3360 ML models were constructed using image series before and after NCRT, respectively. The optimal pre-NCRT ML model, constructed from imaging series before NCRT, was RF; whereas the optimal post-NCRT model, derived from imaging series after NCRT, was MLP. The areas under the curve for the optimal RF and MLP models demonstrated values of 0.609 and 0.857, respectively, in the cross-validation cohort, with corresponding values of 0.659 and 0.841 observed in the independent test cohort. Delong tests showed that the predictive efficacy of the post-NCRT model was statistically higher than that of the pre-NCRT model (p < 0.05). Radiomics model developed by TR-CEUS images after NCRT demonstrated high predictive performance for TRG, thereby facilitating precise evaluation of therapeutic response to NCRT in LARC patients.

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