Prostate cancer is the second leading cause of cancer-related mortality among men, after lung cancer [1]. When it metastasises, the 5-year survival rate drops from nearly 100% to just 30% [2]. If the cancer returns after primary therapy, it is termed biochemically recurrent prostate cancer, marked by an increase in PSA levels [3, 4]. This study aims to identify at-risk patients by developing predictive models for overall survival, using radiomic features from baseline [68Ga]Ga-PSMA-11 PET/CT scans combined with clinical features. An example of a [68Ga]Ga-PSMA-11...

Study Design: Multicentre study conducted at Sir Charles Gairdner Hospital and Fiona Stanley Hospital. Participants: 180 patients with biochemically recurrent metastatic prostate cancer were included. Imaging: All patients underwent [68Ga]Ga-PSMA-11 PET/CT imaging. Analysis: The analysis consisted of two phases: univariable and multivariable. The univariable analysis, shown in Figure 2, correlated individual features with overall survival using Kaplan-Meier curves and Cox proportional hazards models. The multivariable analysis, displayed in Figure 3, involved creating Cox proportional hazards models for predicting overall survival using clinical features only, radiomic...

Univariable Analysis 68 out of 89 radiomic features (76%) from the largest lesion were significantly correlated with overall survival (p-value < 0.05) using a univariable Cox proportional hazards model. Top radiomic features with the highest C-indices are listed below (see corresponding Kaplan-Meier curves in Figure 4): Total lesional uptake: C-index = 0.707, p=7.26×10-8 Total lesional volume: C-index = 0.704, p=2.16×10-7 original_gldm_DependenceEntropy: C-index = 0.704, p = 3.12×10-7 6 out of 8 clinical features (75%) were highly correlated with overall survival: prostatectomy vs radiotherapy, number of...

Univariable analysis revealed that many radiomic features had significant prognostic value. However, when included in multivariable models, these radiomic features did not improve predictive accuracy beyond that of clinical features alone. This suggests that radiomic features may not provide additional prognostic power for predicting overall survival in this cohort, and clinical features continue to be the most effective tool for prognosis.

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