A fully automated AI-based method for tumour detection and quantification on [¹⁸F]PSMA-1007 PET-CT images in prostate cancer.

ABSTRACT: In this study, we further developed an artificial intelligence (AI)-based method for the detection and quantification of tumours in the prostate, lymph nodes and bone in prostate-specific membrane antigen (PSMA)-targeting positron emission tomography with computed tomography (PET-CT) images. A total of 1064 [18F]PSMA-1007 PET-CT scans were used (approximately twice as many compared to our previous AI model), of which 120 were used as test set. Suspected lesions were manually annotated and used as ground truth. A convolutional neural network was developed and trained. The sensitivity and positive predictive value (PPV) were calculated using two sets of manual segmentations as reference. Results were also compared to our previously developed AI method. The correlation between manually and AI

SUBMITTER: Tragardh E 

PROVIDER: S-EPMC12367631 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature