Academic radiology · 2026
Multiscale Prediction of Tumor Micronecrosis and Progression in Clear Cell Renal Cell Carcinoma Based on Artificial Intelligence: A Multicenter Cohort Study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Lv Dingyang, Li Jinshuai, Wang Zhengkun, Guo Zhiwei, Fang Hanguang, Yao Xiaomei, Pang Lei, Gao Jiacheng, Zhao Tiantian, Shuang Weibing
Аннотация
RATIONALE AND OBJECTIVES: Tumor micronecrosis (TM) represents a critical pathological feature influencing the prognosis and surgical management of clear cell renal cell carcinoma (ccRCC). This multicenter study aimed to develop and validate a Multimodal Predictive System (MPS) integrating radiomics and deep learning (DL) for accurate noninvasive assessment of TM and progression in ccRCC.
MATERIALS AND METHODS: A total of 468 ccRCC patients from three independent institutions were divided into a training set (n = 198), validation set (n = 85), and an external test set (n = 185). Radiomics features were extracted from intratumoral and peritumoral regions. Multiple machine learning (ML) classifiers were constructed and evaluated. In parallel, 2D, 2.5D, and 3D DL models based on ResNet34 were trained using the same regions of interest (ROIs). The optimal models from each modality were integrated via stacking ensemble learning to construct the MPS. The model interpretability was analyzed using SHapley Additive exPlanations (SHAP). Kaplan-Meier analysis was used to evaluate the Progression-free survival (PFS) of patients.
RESULTS: The Rad-Peri-1mm model achieved the best performance (AUC=0.765 in test set). Among DL models, the 2.5D-Peri-5mm model showed superior generalizability (AUC=0.763 in test set). The MPS integrating the optimal radiomics and optimal DL models via an support vector machine (SVM) stacking strategy outperformed single-modality models, achieving AUC of 0.774 in test set. SHAP analysis revealed that features derived from the Rad-Peri-1 mm model contributed most significantly to TM prediction. The Kaplan-Meier analysis revealed a better prognosis for patients in the low-risk group.
CONCLUSION: The MPS provides a robust, noninvasive tool for preoperative prediction of TM and progression in ccRCC, supporting individualized treatment decision-making.
MATERIALS AND METHODS: A total of 468 ccRCC patients from three independent institutions were divided into a training set (n = 198), validation set (n = 85), and an external test set (n = 185). Radiomics features were extracted from intratumoral and peritumoral regions. Multiple machine learning (ML) classifiers were constructed and evaluated. In parallel, 2D, 2.5D, and 3D DL models based on ResNet34 were trained using the same regions of interest (ROIs). The optimal models from each modality were integrated via stacking ensemble learning to construct the MPS. The model interpretability was analyzed using SHapley Additive exPlanations (SHAP). Kaplan-Meier analysis was used to evaluate the Progression-free survival (PFS) of patients.
RESULTS: The Rad-Peri-1mm model achieved the best performance (AUC=0.765 in test set). Among DL models, the 2.5D-Peri-5mm model showed superior generalizability (AUC=0.763 in test set). The MPS integrating the optimal radiomics and optimal DL models via an support vector machine (SVM) stacking strategy outperformed single-modality models, achieving AUC of 0.774 in test set. SHAP analysis revealed that features derived from the Rad-Peri-1 mm model contributed most significantly to TM prediction. The Kaplan-Meier analysis revealed a better prognosis for patients in the low-risk group.
CONCLUSION: The MPS provides a robust, noninvasive tool for preoperative prediction of TM and progression in ccRCC, supporting individualized treatment decision-making.