A nomogram integrating multi-channel 2.5D twins-SVT deep learning and radiomics for predicting disease-free survival in patients with locally advanced rectal cancer following primary total mesorectal excision.

OBJECTIVE: To develop a Nomogram for risk stratification of disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing primary total mesorectal excision (TME) without neoadjuvant chemoradiotherapy (nCRT), by integrating a multi-channel 2.5D Twins-SVT deep learning (DL) model and a radiomics model. METHODS: In this retrospective study, a total of 408 patients with pathologically confirmed LARC from two hospitals were included. These patients were divided into a training cohort (n = 189), an internal validation cohort (n = 82), and an external test cohort (n = 137). Radiomic features were extracted from preoperative MRI images, and DL features were captured using a multi-channel 2.5D Twins-SVT network. Univariable and multivariable Cox regression analyses identified prognostically significant clinical factors, which were subsequently integrated with radiomic and 2.5D DL signatures into a predictive Nomogram. The predictive performance of this Nomogram was evaluated by employing the concordance index (C-index), time-dependent area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). RESULTS: The established Nomogram exhibited strong predictive performance regarding disease-free survival (DFS), reflected by C-index values of 0.820 in internal validation and 0.813 in the external testing cohort. When predicting 3-year DFS outcomes, the AUCs in the internal validation and external test cohorts reached 0.864 and 0.816, respectively. Calibration plots demonstrated close concordance between predicted probabilities and actual outcomes. Additionally, DCA confirmed that the Nomogram provided substantial clinical utility across a broad spectrum of clinical decision thresholds. CONCLUSION: This Nomogram is a reliable tool to support clinicians in individualized adjuvant treatment decision-making for these patients.