Magnetic resonance imaging-based radiomics of mesorectum for predicting extramural venous invasion in patients with rectal cancer: a bi-centric study

Objectives To develop and validate a magnetic resonance imaging (MRI)-based radiomics model of the mesorectum for predicting extramural venous invasion (EMVI) in patients with rectal cancer (RC). Methods A retrospective study included 238 patients with RC from two hospitals between May 2020 and March 2023. Patients were divided into a training set (n = 114, from institution 1), an internal validation set (n = 48, from institution 1), and an external validation set (n = 76, from institution 2). A total of 963 radiomics features were extracted from the mesorectum region using T2-weighted imaging (T2WI). The radiomics model was developed using the methods of the minimum redundancy of the maximum relevance (mRMR) and the least absolute shrinkage (LASSO) regression. After univariate and multivariate logistic analysis, a clinical model was constructed based on clinical characteristics. A combined model was built and demonstrated as a nomogram. These models were evaluated by discrimination, calibration, and clinical application. Results Among 238 patients, 98 (41.1%) were EMVI-positive. The Area Under the Curve (AUC) values for the clinical, radiomics, and combined models, respectively, were 0.65, 0.85, and 0.88 for the training set (95% CI: 0.81–0.94); 0.60, 0.81, and 0.81 for the internal validation set (95% CI: 0.68–0.95); and 0.60, 0.78, and 0.82 for the external validation set (95% CI: 0.72–0.91). Conclusion This study presents a model for predicting the EMVI status in patients with rectal cancer. The combined model, which incorporates both a mesorectal radiomics signature from T2WI and the clinical predictor of serum neutrophil count, demonstrated superior discrimination, calibration, and clinical utility compared to models based on either clinical or radiomics features alone. The non-invasive tool shows promise for aiding in preoperative risk stratification and guiding clinical decision-making.