European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology · 2025
Development and validation of a user-friendly prediction tool for preoperative T-Staging in gallbladder Cancer: A multicenter study using contrast-enhanced CT-Based fusion models.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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He Xiaodong, Long Yin, Huang Jue, Liao Jianguo, Zhang Lei
Аннотация
INTRODUCTION: Accurate T-staging of gallbladder cancer (GBC) is critical for surgical planning; however, existing imaging techniques have limited identification accuracy. This study aimed to develop a robust model and prediction tool to address these limitations.
MATERIALS AND METHODS: A retrospective cohort of 189 GBC patients from two institutions between January 2014 and December 2023 were analyzed. Patients were randomly assigned to internal training (ITC, n = 111), internal validation (IVC, n = 48), and temporal validation (TVC, n = 30) cohorts. Radiomics (Rad) and deep learning (DL) features were extracted from arterial and portal venous sequences, alongside clinical data, were used to construct pre- and post-fusion models. A weighted GBC T-staging (wGBCT) model was developed by combining probabilities from four modalities in the TVC: Clinical, Clinical + Rad(AP + PVP), Clinical + DL(AP + PVP), and Clinical + Rad + DL(CRDL), using a weighted averaging method. This model was validated and implemented as a user-friendly prediction tool.
RESULTS: The CRDL model achieved AUCs of 1.0 in the ITC and 0.913 in the IVC. In the TVC, the prediction tool attained an accuracy of 0.867, while the wGBCT model outperformed the CRDL model with an AUC of 0.910 (95 % CI: 0.792-1.000) compared to 0.869 (95 % CI:0.729-1.000). The wGBCT model also demonstrated superior sensitivity (1.0) and F1-score (0.867). Calibration curve analysis confirmed strong alignment, and decision curve analysis indicated the highest clinical net benefit at risk thresholds below 0.6.
CONCLUSIONS: The wGBCT model, integrating multimodal features and a user-friendly prediction tool, demonstrated high predictive accuracy and stability for preoperative T-staging of GBC, providing a valuable reference for individualized surgical planning.
MATERIALS AND METHODS: A retrospective cohort of 189 GBC patients from two institutions between January 2014 and December 2023 were analyzed. Patients were randomly assigned to internal training (ITC, n = 111), internal validation (IVC, n = 48), and temporal validation (TVC, n = 30) cohorts. Radiomics (Rad) and deep learning (DL) features were extracted from arterial and portal venous sequences, alongside clinical data, were used to construct pre- and post-fusion models. A weighted GBC T-staging (wGBCT) model was developed by combining probabilities from four modalities in the TVC: Clinical, Clinical + Rad(AP + PVP), Clinical + DL(AP + PVP), and Clinical + Rad + DL(CRDL), using a weighted averaging method. This model was validated and implemented as a user-friendly prediction tool.
RESULTS: The CRDL model achieved AUCs of 1.0 in the ITC and 0.913 in the IVC. In the TVC, the prediction tool attained an accuracy of 0.867, while the wGBCT model outperformed the CRDL model with an AUC of 0.910 (95 % CI: 0.792-1.000) compared to 0.869 (95 % CI:0.729-1.000). The wGBCT model also demonstrated superior sensitivity (1.0) and F1-score (0.867). Calibration curve analysis confirmed strong alignment, and decision curve analysis indicated the highest clinical net benefit at risk thresholds below 0.6.
CONCLUSIONS: The wGBCT model, integrating multimodal features and a user-friendly prediction tool, demonstrated high predictive accuracy and stability for preoperative T-staging of GBC, providing a valuable reference for individualized surgical planning.