Academic radiology · 2026
A Nomogram Integrating Deep Learning and Immunoscore for Predicting Distant Metastasis and Prognosis in Rectal Cancer.
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Jiang Hao, Sun Miao, Guo Wei, Lin Xue, Qin Yudie, Wu Qiong, Sun Zhongqi, Zhao Jie, Qiao Guanzhong, Hu Hongbo, Shi Zhongxing, Zhuang Qingchun, Wang Ying, Jiang Huijie
Аннотация
RATIONALE AND OBJECTIVES: To evaluate the performance of nomogram integrating MRI-based deep learning (DL) and immunoscore (IS) for predicting distant metastasis (DM) and prognostic stratification in rectal cancer (RC).
MATERIALS AND METHODS: This retrospective single-center study included 306 RC patients (mean age, 59.7±11.2 years) who underwent MRI and surgery. A DL model was developed from preoperative T2-weighted images. The IS was determined on surgical specimens via immunohistochemistry for CD3+ and CD8+ T cells. A nomogram was constructed by integrating the DL_score, IS, and clinical predictors identified by Cox regression. Model performance was assessed using the concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis. Feature importance was measured by the SHapley Additive exPlanations method and patient stratification was assessed through Kaplan-Meier analysis.
RESULTS: At the cutoff date, 97 (31.7%) had experienced DM or death, with a median follow-up of 45.8 months (IQR, 33.9-85.8). Patients with lower IS showed significantly higher DM risk and worse DM-free survival (P < 0.001). The DL_score, IS, and clinical predictors were used to construct the nomogram with C-indexes of 0.807 and 0.882 in training and test set, with AUCs of 0.922 and 0.914 for predicting 5-year DM-free survival. Nomogram effectively stratified patients into high- and low-risk groups and high-risk subgroups had significantly worse DM-free survival (Log-rank, P < 0.001).
CONCLUSIONS: The integrated nomogram demonstrates strong performance for individualized DM risk prediction and prognostic stratification in RC, providing a clinically useful tool for postoperative management.
MATERIALS AND METHODS: This retrospective single-center study included 306 RC patients (mean age, 59.7±11.2 years) who underwent MRI and surgery. A DL model was developed from preoperative T2-weighted images. The IS was determined on surgical specimens via immunohistochemistry for CD3+ and CD8+ T cells. A nomogram was constructed by integrating the DL_score, IS, and clinical predictors identified by Cox regression. Model performance was assessed using the concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis. Feature importance was measured by the SHapley Additive exPlanations method and patient stratification was assessed through Kaplan-Meier analysis.
RESULTS: At the cutoff date, 97 (31.7%) had experienced DM or death, with a median follow-up of 45.8 months (IQR, 33.9-85.8). Patients with lower IS showed significantly higher DM risk and worse DM-free survival (P < 0.001). The DL_score, IS, and clinical predictors were used to construct the nomogram with C-indexes of 0.807 and 0.882 in training and test set, with AUCs of 0.922 and 0.914 for predicting 5-year DM-free survival. Nomogram effectively stratified patients into high- and low-risk groups and high-risk subgroups had significantly worse DM-free survival (Log-rank, P < 0.001).
CONCLUSIONS: The integrated nomogram demonstrates strong performance for individualized DM risk prediction and prognostic stratification in RC, providing a clinically useful tool for postoperative management.