K. Molin: Predicting Outcomes for Patients with ProstateCancer using [68Ga]Ga-PSMA-11 PET/CT Imaging

Kaylee Molin's Masters thesis presentation, for a Medical Physics degree at UWA

Thesis title: Predicting Outcomes for Patients with Prostate Cancer using [68Ga]Ga-PSMA-11 PET/CT Imaging

Supervised by: Dr. Jake Kendrick, Adj/Prof. Martin A. Ebert, Dr. G.M. Hassan
Abstract
Objectives: Prostate cancer is a significant global health issue due to its high incidence and poor outcomes in metastatic disease. This study aims to develop models predicting overall survival and prostate-specific antigen (PSA) progression-free survival for patients with biochemically recurrent prostate cancer, potentially aiding in identifying high-risk patients and enabling tailored treatment options.

Methods: A retrospective, multicentre cohort of 238 men who underwent gallium-68 prostatespecific membrane antigen PET/CT scans was analysed, with many receiving a follow-up scan at a median of 6 months. Lesions were semi-automatically segmented, and radiomic features were extracted from baseline lesions. Univariable analysis using Kaplan-Meier curves and Cox proportional hazards models was first performed to assess the relationship between individual radiomic and clinical features with overall survival. Following this, multivariable models were developed for clinical, radiomic, and various combined features, resulting in 13 predictive models. Follow-up scans classified patients as having progressive or nonprogressive disease using RECIP 1.0 and PPP criteria, which were compared based on overall survival and PSA progression-free survival. Finally, patients were stratified by treatment type to evaluate the impact of treatment on the progression criteria.

Results: Univariable analysis identified significant correlations with overall survival for 6 of 8 clinical features and 68 of 89 radiomic features, including age, disease stage, total lesional uptake, and volume. The optimism-corrected concordance indices were 0.722 (95% CI: 0.653–0.784) for the clinical model, 0.681 (95% CI: 0.616-0.745) for the radiomics model, and 0.704 (95% CI: 0.648-0.768) for the combined model. Follow-up scans showed that patients with progressive disease, as classified by RECIP 1.0, had significantly shorter median survival (60.9 months vs. not reached, p=0.00005) and a higher risk of death (HR = 3.92, p=0.00016). For PSA progression-free survival, RECIP 1.0 had a median of 24.5 months compared to 57.8 months in the non-progressive group (p=0.005), with a HR of 2.31 (p=0.0058). PPP criteria performed worse in both analyses.

Conclusion: Many radiomic features demonstrated significant prognostic value. However, the multivariable model using only clinical features outperformed all others, indicating that clinical features remain the most important factors for predicting patient outcomes. Additionally, RECIP 1.0 proved to be a more e!ective criterion than PPP for classifying patient progression with respect to the primary endpoints of overall survival and PSA progressionfree survival.
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