Academic radiology · 2025
A Transformer-Based Model Integrating Intratumoral Habitats and Peritumoral Radiomics for Detecting Pelvic Lymph Node Metastasis in Prostate Cancer.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Cao Jinming, Feng Xu, Liu Ruishan, Luo Tong, Yang Lian, Li Haiqing, Wang Fei, Lin Ping, Xiang Ye, Yang Jianqiong, Fu Yu, Li Fan
Аннотация
RATIONALE AND OBJECTIVES: Pelvic lymph node metastasis (PLNM) is a critical factor in prostate cancer (PCa) treatment decisions. Current imaging and clinical nomograms remain limited by suboptimal sensitivity and frequent underdiagnosis. This study aimed to develop and validate a transformer-based model integrating intratumoral habitat and peritumoral radiomics features for noninvasive preoperative PLNM prediction.
METHODS: A retrospective cohort of 867 PCa patients from four centers who underwent radical prostatectomy and pelvic lymph node dissection was enrolled. Patients were split into training (n = 437), internal validation (n = 125), and external test (n = 305) cohorts. Radiomic features were extracted from tumor habitats and peritumoral rings (3/6/9 mm). Unimodal models were constructed and fused using a transformer architecture that combined habitat, optimal peritumoral, and clinical variables. Performance was assessed using AUC, calibration curves, and decision curve analysis (DCA). Feature importance was interpreted via SHAP values.
RESULTS: The habitat model outperformed all unimodal models (AUC 0.788-0.834) and both radiologists (5+ and 10+ years' experience), followed by the 6-mm peritumoral model (AUC: 0.729-0.835). The fusion model achieved superior performance across cohorts (AUC: 0.824-0.917; accuracy: 0.797-0.840; sensitivity: 0.869-0.939) and demonstrated good calibration (P > 0.05). DCA confirmed greater net clinical benefit. Performance remained robust across T-stage and Gleason Grade Group subgroups.
CONCLUSION: The transformer-based fusion model offers accurate, sensitive, and interpretable prediction of PLNM, reducing underdiagnosis and overdiagnosis and supporting individualized clinical decision-making.
METHODS: A retrospective cohort of 867 PCa patients from four centers who underwent radical prostatectomy and pelvic lymph node dissection was enrolled. Patients were split into training (n = 437), internal validation (n = 125), and external test (n = 305) cohorts. Radiomic features were extracted from tumor habitats and peritumoral rings (3/6/9 mm). Unimodal models were constructed and fused using a transformer architecture that combined habitat, optimal peritumoral, and clinical variables. Performance was assessed using AUC, calibration curves, and decision curve analysis (DCA). Feature importance was interpreted via SHAP values.
RESULTS: The habitat model outperformed all unimodal models (AUC 0.788-0.834) and both radiologists (5+ and 10+ years' experience), followed by the 6-mm peritumoral model (AUC: 0.729-0.835). The fusion model achieved superior performance across cohorts (AUC: 0.824-0.917; accuracy: 0.797-0.840; sensitivity: 0.869-0.939) and demonstrated good calibration (P > 0.05). DCA confirmed greater net clinical benefit. Performance remained robust across T-stage and Gleason Grade Group subgroups.
CONCLUSION: The transformer-based fusion model offers accurate, sensitive, and interpretable prediction of PLNM, reducing underdiagnosis and overdiagnosis and supporting individualized clinical decision-making.