# Magnetic resonance imaging‐based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study

## TL;DR

This study shows that combining MRI scans with radiomics can help predict how well rectal cancer patients will respond to treatment and their long-term outcomes.

## Contribution

A combined radiomics model using MRI and SVM was developed and validated across multiple centers for predicting treatment response in rectal cancer.

## Key findings

- The combined model achieved an AUC of 0.799 in the training dataset for predicting good treatment response.
- Patients predicted to have a good response had better disease-free survival according to Kaplan-Meier analysis.
- The model was validated in two external and one prospective dataset, showing moderate performance.

## Abstract

Our study investigated whether magnetic resonance imaging (MRI)‐based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR‐detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760–0.838), 0.797 (95% CI, 0.733–0.860), 0.754 (95% CI, 0.678–0.829), and 0.727 (95% CI, 0.641–0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan–Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease‐free survival than those with a low probability. This radiomics model was developed based on large‐sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.

This study constructed a combined model with clinical MRI features and radiomics signature generated by Support Vector Machine (SVM) algorithm, and showed promising discrimination for the prediction of treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients. The model was then validated in two external validation datasets and a prospective validation dataset, exhibited moderate performance for predicting good response, and was valuable for prognosis prediction.