European journal of nuclear medicine and molecular imaging · 2025
Interpretable multimodal deep learning model for predicting post-surgical international society of urological pathology grade in primary prostate cancer.
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Jiang Jiamei, Fan Zhiyan, Jiang Shen, Chen Xia, Guo Hongyu, Dong Shuangyong, Jiang Tianan
Аннотация
PURPOSE: To address heterogeneity in prostate cancer (PCa) pathological grading, we developed an interpretable multimodal fusion model integrating 18F prostate-specific membrane antigen (18F-PSMA)-targeted positron emission tomography/computed tomography (18F-PSMA-PET/CT) imaging features with clinical variables for predicting post-surgical ISUP grade (psISUP ≥ 4 vs. < 4).
METHODS: This retrospective study analyzed 222 patients with PCa (2020-2024) undergoing 18F-PSMA PET/CT. We constructed a deep transfer learning framework incorporating radiomic features from PET/CT and clinical parameters. Model performance was validated against three established methods and preoperative biopsy Gleason scores. Additionally, SHapley Additive exPlanations (SHAP) values elucidated feature contributions, and a radiomic nomogram was developed for clinical translation.
RESULTS: The fusion model achieved superior discrimination in psISUP grading (test set area under the curve (AUC) = 0.850, 95% confidence interval [CI] 0.769-0.932; validation set AUC = 0.833, 95% CI 0.657-1.000), significantly outperforming preoperative Gleason scores. SHAP analysis identified PSMA uptake heterogeneity and PSA density as key predictive features. The nomogram demonstrated clinical interpretability through visualised risk stratification.
CONCLUSION: Our deep learning-based multimodal fusion model enables accurate preoperative prediction of aggressive PCa pathology (ISUP ≥ 4), potentially optimising surgical planning and personalised therapeutic strategies. The interpretable framework enhances clinical trustworthiness in artificial intelligence-assisted decision-making.
METHODS: This retrospective study analyzed 222 patients with PCa (2020-2024) undergoing 18F-PSMA PET/CT. We constructed a deep transfer learning framework incorporating radiomic features from PET/CT and clinical parameters. Model performance was validated against three established methods and preoperative biopsy Gleason scores. Additionally, SHapley Additive exPlanations (SHAP) values elucidated feature contributions, and a radiomic nomogram was developed for clinical translation.
RESULTS: The fusion model achieved superior discrimination in psISUP grading (test set area under the curve (AUC) = 0.850, 95% confidence interval [CI] 0.769-0.932; validation set AUC = 0.833, 95% CI 0.657-1.000), significantly outperforming preoperative Gleason scores. SHAP analysis identified PSMA uptake heterogeneity and PSA density as key predictive features. The nomogram demonstrated clinical interpretability through visualised risk stratification.
CONCLUSION: Our deep learning-based multimodal fusion model enables accurate preoperative prediction of aggressive PCa pathology (ISUP ≥ 4), potentially optimising surgical planning and personalised therapeutic strategies. The interpretable framework enhances clinical trustworthiness in artificial intelligence-assisted decision-making.