Deep learning-based PSMA PET segmentation repeatability: A post-hoc analysis of a single-center, prospective, test-retest trial.

ABSTRACT:

Objectives

The primary aim of this study was to quantify the subject-level test-retest repeatability of artificial intelligence (AI)-derived PSMA PET imaging biomarkers using a previously developed model. The secondary aim was to assess the performance of this segmentation model, which was trained on [⁶⁸ Ga]Ga-PSMA-11 PET scans, on [¹⁸F]F-PSMA-1007 PET scans.

Methods

This was a post-hoc analysis of a prospective, single-center, test-retest trial. Seventeen patients with metastatic prostate cancer (mPCa) were randomised into groups, either receiving the same tracer ([⁶⁸ Ga]Ga-PSMA-11 or [¹⁸F]F-PSMA-1007) for both scans (intra-tracer group, n = 9) or a different tracer (inter-tracer group, n = 8). Scans were delineated using a fully

SUBMITTER: Kendrick J 

PROVIDER: S-EPMC12932408 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature