Development of an MRI ‐Based Comprehensive Model Fusing Clinical, Habitat Radiomics, and Deep Learning Models for Preoperative Identification of Tumor Deposits in Rectal Cancer

BACKGROUND: Tumor deposits (TDs) are an important prognostic factor in rectal cancer. However, integrated models combining clinical, habitat radiomics, and deep learning (DL) features for preoperative TDs detection remain unexplored. PURPOSE: To investigate fusion models based on MRI for preoperative TDs identification and prognosis in rectal cancer. STUDY TYPE: Retrospective. POPULATION: Surgically diagnosed rectal cancer patients (n = 635): training (n = 259) and internal validation (n = 112) from center 1; center 2 (n = 264) for external validation. FIELD STRENGTH/SEQUENCE: 1.5/3T, T2-weighted image (T2WI) using fast spin echo sequence. ASSESSMENT: Four models (clinical, habitat radiomics, DL, fusion) were developed for preoperative TDs diagnosis (184 TDs positive). T2WI was segmented using nnUNet, and habitat radiomics and DL features were extracted separately. Clinical parameters were analyzed independently. The fusion model integrated selected features from all three approaches through two-stage selection. Disease-free survival (DFS) analysis was used to assess the models' prognostic performance. STATISTICAL TESTS: Intraclass correlation coefficient (ICC), logistic regression, Mann-Whitney U tests, Chi-squared tests, LASSO, area under the curve (AUC), decision curve analysis (DCA), calibration curves, Kaplan-Meier analysis. RESULTS: The AUCs for the four models ranged from 0.778 to 0.930 in the training set. In the internal validation cohort, the AUCs of clinical, habitat radiomics, DL, and fusion models were 0.785 (95% CI 0.767-0.803), 0.827 (95% CI 0.809-0.845), 0.828 (95% CI 0.815-0.841), and 0.862 (95% CI 0.828-0.896), respectively. In the external validation cohort, the corresponding AUCs were 0.711 (95% CI 0.599-0.644), 0.817 (95% CI 0.801-0.833), 0.759 (95% CI 0.743-0.773), and 0.820 (95% CI 0.770-0.860), respectively. TDs-positive patients predicted by the fusion model had significantly poorer DFS (median: 30.7 months) than TDs-negative patients (median follow-up period: 39.9 months). DATA CONCLUSION: A fusion model may identify TDs in rectal cancer and could allow to stratify DFS risk. TECHNICAL EFFICACY STAGE: 3.