Murat ÖZOĞUL, Kamil CANPOLAT, Eşref BAŞARAN, Murat BAYKARA
İstanbul Medical Journal - 2026;27(3):245-251
Introduction: This study investigated the predictability of treatment response to neoadjuvant chemoradiotherapy (nCRT) using texture analysis derived from pre-treatment magnetic resonance imaging (MRI) in patients diagnosed with locally advanced rectal cancer (LARC). Methods: A total of 47 patients (28 males and 19 females) diagnosed with LARC who received nCRT between 2018 and 2025 were retrospectively reviewed in the hospital archives. Texture analysis was performed on pretreatment MR images, specifically on T2-weighted images, apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted images. For the assessment of treatment response, patients were classified according to the modified Ryan tumor regression grading system: those with Ryan grades 0 and 1 were assigned to the favorable response group, while those with Grades 2 and 3 were assigned to the poor response group. Statistical analysis was conducted using the Mann-Whitney U test, receiver operating characteristic curve analysis, and multivariable logistic regression. Results: Statistically significant differences were detected between the responding and non-responding groups across 44 texture parameters in the three sequences. The multivariable logistic regression model was statistically significant (p<0.001). A combined predictive model, including the contrast of matrix from the ADC sequence and the contrast of matrix and energy of matrix from the contrast-enhanced T1-weighted sequence, explained 74.3% of the variance in treatment response and correctly classified 91.3% of the patients. Conclusion: Texture analysis derived from pre-treatment magnetic MR imaging can predict the response to neoadjuvant therapy with high accuracy in patients with LARC. This non-invasive approach demonstrates considerable potential as a clinical tool to guide personalized treatment strategies, thereby facilitating the selection of optimal therapeutic regimens for patients who are unlikely to benefit from standard protocols.
