Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

TLDR

To develop and validate a radiomics model for predicting pathologic complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. The study extracted 2,252 radiomic features from pre‑ and post‑treatment T2‑weighted and diffusion‑weighted MRI of 222 patients, selected features with t‑test and LASSO, built a support‑vector‑machine signature, and combined it with clinicopathologic factors in a logistic‑regression model. The radiomics signature of 30 features and the combined model (signature plus tumor length) achieved excellent discrimination (AUC 0.9756, 95% CI 0.9185‑0.9711) and good calibration in validation, with decision‑curve analysis supporting its clinical utility for identifying patients who could omit surgery after chemoradiotherapy. Published in Clinical Cancer Research, 23(23):7253‑62, ©2017 AACR.

Abstract

Purpose: To develop and validate a radiomics model for evaluating pathologic complete response (pCR) to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC).Experimental Design: We enrolled 222 patients (152 in the primary cohort and 70 in the validation cohort) with clinicopathologically confirmed LARC who received chemoradiotherapy before surgery. All patients underwent T2-weighted and diffusion-weighted imaging before and after chemoradiotherapy; 2,252 radiomic features were extracted from each patient before and after treatment imaging. The two-sample t test and the least absolute shrinkage and selection operator regression were used for feature selection, whereupon a radiomics signature was built with support vector machines. Multivariable logistic regression analysis was then used to develop a radiomics model incorporating the radiomics signature and independent clinicopathologic risk factors. The performance of the radiomics model was assessed by its calibration, discrimination, and clinical usefulness with independent validation.Results: The radiomics signature comprised 30 selected features and showed good discrimination performance in both the primary and validation cohorts. The individualized radiomics model, which incorporated the radiomics signature and tumor length, also showed good discrimination, with an area under the receiver operating characteristic curve of 0.9756 (95% confidence interval, 0.9185-0.9711) in the validation cohort, and good calibration. Decision curve analysis confirmed the clinical utility of the radiomics model.Conclusions: Using pre- and posttreatment MRI data, we developed a radiomics model with excellent performance for individualized, noninvasive prediction of pCR. This model may be used to identify LARC patients who can omit surgery after chemoradiotherapy. Clin Cancer Res; 23(23); 7253-62. ©2017 AACR.