Radiomics-based Artificial Intelligence in Rectal Cancer: Pathway to Surgical Integration

Accurate evaluation of tumor response after neoadjuvant chemoradiotherapy (nCRT) is pivotal in managing locally advanced rectal cancer.Identifying patients who achieve a pathological complete response (pCR) enables the implementation of organ-preserving strategies such as watch and wait, thereby reducing morbidity and improving quality of life.Because pCR is linked to survival and lower recurrence, precise prediction is essential.However, conventional tools, such as clinical examination, endoscopy, and magnetic resonance imaging (MRI), show limited sensitivity and considerable interobserver variation, and optimized MRI fails to distinguish clinical from true pCR, complicating surgical decision-making. 1,2adiomics offers a promising solution by extracting quantitative MRI-based features that yield biomarkers of tumor heterogeneity and treatment-related change.Machine learning analyzes these data, detecting texture, entropy, or morphologic patterns suggestive of residual disease, fibrosis, or necrosis.Such biomarkers refine the distinction between clinical and pathological response, although standardized methods and prospective validation remain prerequisites for clinical adoption. 3,4vidence for radiomics is expanding rapidly.A recent metaanalysis encompassing over 10,000 patients across 35 studies reported a pooled area under the curve of approximately