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Development and multicohort external validation of a preoperative [18F]PSMA-1007 PET-derived deep learning score and
Tiancheng Li1, Nina Xu2, Xiaofang Yan1
1Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Purpose:
To develop and validate a preoperative [18F]PSMA-1007 PET-derived deep learning score (DLS) and an integrated model combining DLS, D'Amico risk classification, and SUVmax for predicting biochemical recurrence-free survival (BRFS) after radical prostatectomy (RP).
Materials And Methods:
This retrospective study included 697 patients with prostate cancer who underwent preoperative [18F]PSMA-1007 PET/CT before RP at three campuses within one healthcare network: Qingchun (training cohort, n = 445), Yuhang (validation cohort 1, n = 190), and Zhijiang (validation cohort 2, n = 62). Five convolutional neural networks were trained to predict 3-year biochemical recurrence from PET images, and the output of the best-performing network was defined as the DLS. Four preoperative prognostic models were evaluated: DLS alone, D'Amico classification alone, D'Amico plus SUVmax, and D'Amico plus SUVmax plus DLS. Performance was assessed using Harrell's C-index, inverse-probability-of-censoring-weighted time-dependent AUC, calibration, decision curve analysis, and nested model comparisons.
Results:
VGG19 showed the best fixed-time classification performance, with 3-year ROC AUCs of 0.834, 0.755, and 0.723 in the training and two validation cohorts, respectively. A higher DLS was associated with shorter BRFS in all cohorts. Biopsy ISUP Grade Group was significantly associated with BRFS (global P < 0.001). In multivariable analysis, intermediate- and high-risk D'Amico groups, SUVmax, and DLS were independently associated with BRFS. The integrated model achieved C-indices of 0.846, 0.806, and 0.774 and 36-month AUCs of 0.853, 0.801, and 0.779 in the training and two validation cohorts, respectively, outperforming D'Amico classification alone and D'Amico plus SUVmax.
Conclusion:
A fully preoperative model integrating an [18F]PSMA-1007 PET-derived DLS with D'Amico risk classification and SUVmax improved prediction of BRFS after RP. The model may support preoperative risk counseling and postoperative surveillance planning.