ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026
Development and multicohort external validation of a preoperative [18F]PSMA-1007 PET-derived deep learning score and multimodal model for predicting biochemical recurrence-free survival after radical prostatectomy.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo 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
methodsThis 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.
resultsVGG19 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.
conclusionA 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.
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