AI-Based Prediction of Pathological Response in Rectal Cancer Patients Receiving Total Neoadjuvant Therapy

This is a retrospective observational study that evaluated 93 patients with locally advanced rectal cancer who received total neoadjuvant therapy (TNT) at Etlik City Hospital, Ankara, Turkey, between November 2022 and December 2024. The aim of the study was to investigate whether laboratory markers related to inflammation and nutrition, as well as changes in muscle mass (sarcopenia), could predict treatment response. Patients who completed at least 12 weeks of chemotherapy and radiotherapy followed by curative surgery were included. Blood samples were used to calculate the C-reactive protein to albumin ratio (CAR) and the systemic immune-inflammation index (SII). Sarcopenia was assessed using contrast-enhanced CT images at the L3 vertebral level, obtained before and after treatment. Based on these parameters, two composite scores-CINR-pCR and CINR-Ryan-were developed using multivariable logistic regression. The primary outcomes were pathological complete response (pCR) and favorable tumor regression grade (TRG 0-1). Predictive models were constructed using both conventional statistical methods and artificial intelligence (AI)-based algorithms, including Random Forest. Internal validation was performed via cross-validation, and model performance was assessed by area under the curve (AUC), sensitivity, and specificity. All data used in this study were obtained from existing medical records as part of routine clinical care. No experimental treatments or additional interventions were administered. The results may contribute to optimizing personalized treatment strategies and clinical decision-making in rectal cancer.