Karim Ben Mansour

Prostate cancer represents a highly prevalent malignancy affecting men globally, necessitating precise staging and risk stratification for effective patient management. Multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen positron emission tomography (PSMA PET) have individually revolutionized the diagnosis and management of prostate cancer. Recent developments emphasize the integration of these imaging modalities to improve detection capabilities, inform therapeutic interventions, and facilitate personalized management. This narrative article reviews existing literature on the clinical utilization of mpMRI and PSMA PET in prostate cancer. Key areas encompass initial diagnosis, both local and systemic staging, detection of biochemical recurrence, and their influence in treatment strategies. The integration of mpMRI and PSMA PET offers complementary insights, with mpMRI demonstrating superior capability in local tumor characterization and PSMA PET enhancing the detection of nodal and distant metastases. Quantitative imaging biomarkers, including apparent diffusion coefficient (ADC) and standardized uptake values (SUV), have the potential to improve risk stratification and inform personalized treatment strategies. Hybrid imaging techniques may improve diagnostic accuracy and guide decisions regarding surgery, radiotherapy, and systemic treatment. The integration of mpMRI and PSMA PET allows a potentially transformative advancement in the realm of precision imaging for prostate cancer. This integrated approach can improve diagnostic accuracy, better define disease extent, and support personalized management strategies.

Background/Objectives: To evaluate the diagnostic accuracy of artificial intelligence (AI)-based imaging techniques for liver fibrosis and metabolic dysfunction-associated steatotic liver disease (MASLD). Materials and Methods: We performed a comprehensive search in PubMed, Embase, Cochrane Library, and Web of Science until August 2025. A total of 15 studies (mean age of patients 56 years, 60% male) were included. The risk of bias in the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Diagnostic performance metrics were calculated using a random-effects bivariate model, including the area under the curve (AUC), sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio. Meta-regression analysis was conducted to investigate potential sources of heterogeneity when I² was ≥50%. A p-value < 0.05 was considered statistically significant. Results: For liver fibrosis, pooled sensitivity was 0.85, specificity was 0.81, and AUC was 0.92. For MASLD, sensitivity was 0.86, specificity was 0.95, and AUC was 0.99. Different imaging modalities and AI classifiers caused significant study heterogeneity. To avoid misleading pooled estimates across varied datasets, imaging modality and AI model subgroup analyses were performed. Only three studies were used to estimate MASLD; therefore, considerable between-study heterogeneity should be considered. Conclusions: AI-based imaging modalities demonstrate promising diagnostic accuracy for liver fibrosis and MASLD, warranting further standardization to enhance diagnostic consistency.