Predicting side-specific extraprostatic extension in prostate cancer using an 18F-DCFPyL PSMA-PET/CT-based nomogram.

Area of Science:

• Urology
• Radiology
• Oncology

Background:

• Extraprostatic extension (EPE) in prostate cancer (PCa) impacts surgical planning, particularly for nerve-sparing radical prostatectomy.
• Current multiparametric MRI (mpMRI)-based nomograms have limited accuracy in predicting EPE.
• There is a need for improved predictive models incorporating advanced imaging like PSMA PET/CT.

Purpose of the Study:

• To evaluate the efficacy of ¹⁸F-DCFPyL prostate-specific membrane antigen (PSMA) PET/CT in predicting side-specific EPE in PCa.
• To develop and validate a predictive nomogram integrating PSMA PET/CT parameters with MRI and clinicopathological data.
• To compare the performance of the new PSMA-integrated model against an MRI-only model.

Main Methods:

• A single-center cohort study of 355 patients undergoing robot-assisted radical prostatectomy (RALP) between January 2022 and September 2024.
• Analysis included baseline demographics, PSA, biopsy results, MRI findings, and PSMA PET/CT parameters (SUVmax, EPE, SVI).
• Univariable and multivariable logistic regression were used to identify predictors of EPE, followed by nomogram development and bootstrap validation.

Main Results:

• PSMA PET/CT detected EPE in 5.4% of cases, compared to 18.9% by MRI.
• Higher PSMA SUVmax (median 9.1 vs. 5.4 in EPE-positive vs. negative sides, p<0.001) and specific imaging/pathological features (PIRADS 5, GGG, tumor involvement) were associated with EPE.
• The integrated PSMA+MRI model showed superior performance (AUC 0.7542) compared to the MRI-only model (AUC 0.7350), with strong calibration and clinical utility.

Conclusions:

• A novel nomogram integrating PSMA PET/CT, MRI, and clinicopathological variables significantly improves the prediction of side-specific EPE in prostate cancer.
• PSMA uptake on PET/CT is a strong predictor of EPE, enhancing preoperative assessment.
• Improved EPE prediction can lead to better surgical planning and potentially improved postoperative functional outcomes.