The predictive value of non-enhanced CT radiomics in differentiating early and advanced T-staging of colon cancer

This study aimed to assess the diagnostic value of non-enhanced CT radiomics in preoperatively differentiating early-stage (T1-T2) from locally advanced (T3-T4) colon cancer, addressing the limitations of conventional empirical staging. A retrospective analysis was conducted on 170 patients with surgically confirmed primary colon cancer who underwent non-enhanced CT scans within 1 week before surgery. Three-dimensional segmentation of colonic tumors was performed on the non-enhanced images, followed by automated extraction of radiomic features. Feature selection was executed using the minimum redundancy maximum relevance (mRMR) algorithm, and key features associated with cancer stage were identified using the least absolute shrinkage and selection operator logistic regression. The performance of the radiomics model was compared with conventional T-staging by radiologists. The cohort comprised 170 patients with an average age of 61.69 ± 13.22 years, 43.3% of whom were female, and 75 (44.1%) presented with early-stage disease. Eight radiomic features from non-enhanced imaging were ultimately included. The radiomics model achieved an area under the curve (AUC) of 0.85 (95% confidence interval: 0.78-0.92) in the training set and 0.84 (95% confidence interval: 0.74-0.95) in the test set, with corresponding accuracies of 0.70 and 0.78, sensitivities of 0.87 and 0.87, and specificities of 0.69 and 0.71, respectively. Additionally, in the training set, the radiomics model (AUC = 0.85) significantly outperformed empirical T-staging by radiologists (AUC = 0.71, P < .009). A similar trend was observed in the test set, where the radiomics model (AUC = 0.85) surpassed empirical T-staging (AUC = 0.76), although this difference was not statistically significant (P = .27). Non-enhanced CT radiomics demonstrated superior performance over conventional radiologists' T-staging in distinguishing early from advanced colon cancer stages. © 2025 the Author(s).