ArticleFrontiers in oncology2026
A multivariable prediction model combining 18F-PSMA PET/CT and mpMRI for clinically significant prostate cancer: development and validation.
Article in Frontiers in oncology, 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
Background: Multiparametric MRI (mpMRI) and prostate-specific membrane antigen (PSMA) PET/CT provide complementary information for prostate cancer diagnosis, but optimal integration strategies remain unclear. We aimed to develop and validate a multivariable model combining clinical and imaging parameters to predict clinically significant prostate cancer (csPCa). Methods: This study included 1305 consecutive patients with suspected prostate cancer who underwent both mpMRI and 18F-PSMA PET/CT with biopsy at Peking University First Hospital. The development and temporal validation cohorts were temporally divided; the development cohort was split into training (70%) and internal test (30%) sets. csPCa was defined as Gleason Grade Group ≥2. Predictors were selected using LASSO regression (1-standard error criterion) followed by forward stepwise logistic regression (AIC, Results: The final model retained 3 predictors: PRIMARY score, PI-RADS score, and PSAD. The training AUC was 0.916. At the Youden Index cutoff (≥84%), sensitivity was 79.3% and specificity 76.6%; at the recommended screening cutoff (≥46%), sensitivity reached 96.0%. The AUC was 0.914 (95% CI: 0.882-0.941) in the internal test set and 0.837 (95% CI: 0.778-0.891) in temporal validation. The model showed good calibration (Brier score: 0.096) and superior clinical utility over individual imaging parameters. Conclusions: The multivariable model integrating 18F-PSMA PET/CT and mpMRI parameters provides accurate risk stratification for csPCa and may help optimize biopsy decisions.
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