Machine Learning Radiomics in Computed Tomography for Prediction of Tumor and Nodal Stages in Colorectal Cancer

<b>Background/Objectives</b>: Accurate preoperative TN staging is essential for guiding surgical and adjuvant treatment decisions in colorectal cancer (CRC), yet conventional imaging still faces limitations in reliably distinguishing early from advanced disease. This study aimed to evaluate whether CT-based radiomics combined with machine learning can noninvasively predict both tumor (T) and nodal (N) stages of CRC, and to identify which feature groups most contribute to each task. <b>Methods</b>: Fifty-three patients (55 tumors) with histologically confirmed CRC who underwent preoperative contrast-enhanced CT were retrospectively analyzed. A total of 107 radiomic features were extracted using PyRadiomics version 3.1.0, including shape, first-order, and texture features. Multiple preprocessing strategies-z-score normalization, PCA, and SMOTE-were tested across 11 machine learning classifiers. <b>Results</b>: For T staging, logistic regression using shape-based features achieved a mean sensitivity of 0.721, a specificity of 0.68, a balanced accuracy of 0.70, and an AUC of 0.751. For N staging, the AdaBoost model using texture-based features achieved a sensitivity of 0.742, a specificity of 0.622, a balanced accuracy of 0.682, and an AUC of 0.750. Shape features predominantly contributed to T prediction, while texture matrices drove N prediction, reflecting morphological and microstructural correlates of invasiveness and lymphatic dissemination. <b>Conclusions</b>: CT-based radiomics can quantitatively capture both morphological and textural patterns of tumor behavior, providing a noninvasive framework for preoperative TN staging in CRC.