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Updated: Jan 19, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Generalizable Deep Learning for Prostate Cancer Risk Stratification: Multicenter Study Integrating 18F-PSMA-1007
Cunke Miao1, Houzhang Sun2, Fei Yao2
1The Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (C.M., Y.Y., Y.L.); The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China (C.M., T.H., Z.R., Y.L.).
Background:
Prostate cancer is the second most common cancer in men, with rising mortality rates necessitating precise risk stratification. High-invasive biological features-specifically International Society of Urological Pathology (ISUP) grade, extracapsular extension (EPE), and positive surgical margins (PSM)-are critical for guiding treatment but are difficult to detect due to tumor heterogeneity. Current imaging modalities, including 18F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), have limitations in fully capturing these features. This study aims to develop a few-shot deep learning model (CL-MGNET) that integrates multimodal imaging and clinical data to predict high-risk biological features, optimizing performance even with limited training data.
Materials And Methods:
This retrospective, multicenter study analyzed data from 377 patients: 341 from a primary medical center (Center A) and 36 from an independent external validation cohort (Center B). The study utilized multimodal inputs (PET/CT, mpMRI) and clinical variables to predict ISUP grade, EPE, and PSM. A specialized few-shot deep learning network, CL-MGNET, was designed to fuse these data sources. The model was trained using a restricted subset of 30 patients and subsequently evaluated on both internal and external test sets to assess generalizability across different centers.
Results:
CL-MGNET demonstrated excellent performance in predicting high-invasive biological features (defined as the presence of at least one high-risk feature: ISUP ≥ 3, EPE, or PSM), achieving an internal test AUC of 0.877 and an external validation AUC of 0.872, which significantly outperformed the clinical model with an AUC of 0.792. The model surpassed both single-modality models (PET/CT, mpMRI) and the clinical model. Furthermore, CL-MGNET exhibited strong generalization capability, effectively predicting various high-risk biological features. When clinical variables were integrated, the model's performance improved significantly, exceeding traditional methods.
Conclusion:
The CL-MGNET model, leveraging multimodal imaging data and clinical variables with a few-shot learning approach, successfully predicts high-invasive biological features of prostate cancer with high accuracy, even with limited data. The model's performance across different biological features and medical centers shows its robust generalizability. This method holds great promise for improving prostate cancer diagnosis and risk prediction in data-limited environments.
