Hein Minn Tun, Lin Naing, Owais Ahmed Malik, Muhammad Syafiq Abdullah, Thu Ta, Hanif Abdul Rahman
Prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) provides rich molecular imaging across the prostate cancer care continuum, yet its full utility in artificial intelligence applications remains underexplored. This study developed a multi-task UNETR-based deep learning framework to simultaneously perform lesion segmentation, disease burden quantification, CHAARTED and LATITUDE treatment stratification and survival prediction from a single PSMA PET/CT image. A retrospective cohort study included 212 prostate cancer cases (478 observations, 2018-2024) from The Brunei Cancer Centre, with 160 anonymized whole-body PSMA PET/CT scans and longitudinal clinical data, following the TRIPOD-AI checklist. Preprocessing included bounding-box cropping, Z-score normalization and Otsu-based lesion masking. A simple convolutional neural network (CNN) and a 2D multi-task UNETR model were trained for lesion segmentation, treatment classification and survival prediction. Performance was evaluated using Dice coefficient, multiclass area under the curve (AUC) and concordance index (C-index). Analysis of 205 matched PSMA PET/CT scans from 115 patients over 6 years showed that the CNN achieved strong treatment classification performance (AUC = 0.91), with highest accuracy for active surveillance (AUC = 0.99) and chemotherapy (AUC = 0.97). The multi-task UNETR produced a lower weighted AUC (0.561) but enabled simultaneous lesion segmentation, metastatic burden quantification and CHAARTED/LATITUDE stratification. Deceased patients showed higher Dice scores (mean = 0.556, SD = 0.065) than survivors (mean = 0.324, SD = 0.165). Multi-task UNETR improved clinical interpretability through segmentation-based tumour quantification and disease burden stratification. Larger multicentre datasets and external validation are needed to enhance generalizability and predictive performance.