Deep-learning Evaluation for Enhanced Prognostics - Prostate Specific Membrane Antigen
Creators: name: Jackson, Price
name: McIntosh, Lachlan
name: Buteau, James
name: Hofman, Michael
Publisher: name: Zenodo
Year: 2025
Subjects: PET/CT
PSMA
FDG
Segmentation
MICCAI 2025 challenge

description: Prostate cancer is the most diagnosed and one of the five leading causes of death from cancer. In the advanced form, metastatic castration-resistant prostate cancer (mCRPC) has a high rate of mortality with 30% survival at 5 years and, previously, a limited number of effective treatments after progression from hormone therapy. The development of the molecular target, prostate-specific membrane antigen (PSMA), has enabled new techniques to both image and target systemic disease. Three FDA approved tracers for Positron Emission Tomography (PET) imaging - 68Ga-PSMA-11, 18F-DCFPyL and 18F-rhPSMA-7 - now enable more accurate detection of metastatic spread throughout the body and also selection of patients for radiopharmaceutical therapy (Hofman et al., 2020; Jadvar et al., 2022). Treatment of mCRPC with the 177Lu-PSMA-617 (LuPSMA) presents opportunities to improve quality-of-life and life expectancy in men who have progressed after receiving chemotherapy or hormone therapies (Hofman et al., 2024; Hofman et al., 2021; Sartor et al., 2021).

The expression of targetable receptors in cancer tissues can be quantified by PSMA PET imaging. The addition of FDG PET imaging further informs metabolic activity or hypoxia at the cellular level indicative of an aggressive or potentially poorly responsive form of the disease. Biomarkers from PSMA and FDG imaging have been shown to be predictive of response to 177Lu-PSMA therapy in both the TheraP (Buteau et al., 2022) and LuPSMA trials (Ferdinandus et al., 2020). Ferdinandus et al. reported response classification based on FDG metabolic tumor volume greater or less than 207 ml and PSMA PET SUVmean with a cutoff of 10.55. Buteau et al. retested these baseline predictors in a separate cohort (FDG MTV threshold of 200 ml and PSMA SUVmean of 10). Similar patterns have been observed as lesion-level response predictors (Rosar et  al., 2024) showing that PSMA expression is correlated to response and the ratio of FDG expression relative to PSMA uptake is inversely correlated to treatment response. Correlations between mean PSMA PET tumor SUV at staging with progression free survival, overall survival, and PSA response were also reported in the VISION trial (Kuo et al., 2022). Despite PSMA PET representing a strong predictive and prognostic biomarker in LuPSMA therapy, the manual overhead and lack of robust standardization currently limits routine clinical use.

Existing works applying artificial intelligence methods to mCRPC imaging have yielded promising results, but the reported accuracy has not reached a reliable standard for automated clinical use. Zhao et al. developed a 2.5D U-Net neural network applied to 68Ga-PSMA-11 PET/CT images for scoring total disease volume yielding spatial accuracy of 64.5% for bone lesions and 54.4% for lymph node lesions (Zhao et al., 2020). Using nnU-Net for segmentation of PSMA PET images, Jafari et al. reported lesion level sensitivity exceeding 90%, however volumetric accuracy at voxel level was lower with dice scores in the order of 65-70% (Jafari et al., 2023). Moazemi et al. utilized a similar technique of trained U-Net for whole body disease detection in PSMA PET achieving a similar volumetric accuracy of 0.82 by dice coefficient. In FDG PET imaging, Jemaa et al. have reported dice  agreements as high as 0.886 in non-small cell lung cancer (Jemaa et al., 2020). Results from the public FDG imaging challenge dataset, AutoPET, yielded best-performing dice accuracies of approximately 0.8 in a cohort of melanoma, lung cancer, and lymphoma patients (Gatidis et al., 2023).

While previous research has shown promise in automating the analysis of proven image biomarkers for improved patient selection, further refinements are necessary to deliver a methodology that is fit for clinical use. This project aims to harness the open science philosophy to leverage a large institutional dataset and the collective problem-solving capabilities of the medical imaging and computer science communities to deliver a highly impactful improvement in patient care.