Background/Objectives: Bladder cancer is characterized by high recurrence and progression rates, posing a challenge to current diagnostic and treatment strategies. This review aims to provide a comprehensive overview of emerging technologies, including novel PET tracers, AI-assisted cystoscopy, theragnostics, and molecular biomarkers. Methods: We performed a narrative review of the recent literature focusing on innovations in imaging, AI, theragnostics, and biomarker research relevant to bladder cancer diagnosis and management. Results: Several novel PET tracers, such as 68Ga-PSMA and fibroblast activation protein inhibitor (FAPI), demonstrated potential in improving detection sensitivity. AI-enhanced cystoscopy has shown promise in real-time lesion detection, while theragnostic agents enable combined diagnostic and therapeutic applications. Advances in molecular biomarkers, including circulating Tumor DNA (ctDNA) and gene expression signatures, offer new avenues for patient stratification and monitoring. Conclusions: Integration of advanced imaging, AI, theragnostics, and biomarker analysis may transform bladder cancer management, supporting personalized and more effective care strategies.

Background/Objectives: Prostate cancer is the most prevalent malignancy in men and remains a leading cause of cancer-related mortality worldwide. Conventional imaging modalities exhibit limited sensitivity, particularly in the context of disease recurrence and advanced disease. Methods: A narrative review was conducted of studies published between 2015 and 2025, identified through PubMed, Embase, and Cochrane. Eligible publications addressed advanced imaging techniques, PSMA-targeted diagnostics and therapies, radiogenomics, liquid biopsy approaches, and artificial intelligence applications and personalized medicine. Preclinical studies, single case reports, and conference abstracts without full text were excluded. Results: PSMA PET/CT outperforms conventional imaging for detection, and restaging, influencing clinical management across disease stages. Lutetium-177–PSMA-617 has become the standard radioligand therapy for metastatic castration-resistant prostate cancer, whereas alpha-emitting agents remain under clinical investigation. Radiogenomics and liquid biopsy assays (ctDNA, CTCs, AR-V7) provide complementary molecular insights. Artificial intelligence enhances imaging interpretations, standardization, and reproducibility, while multimodal data integration supports individualized risk stratification. Integrative models combining imaging, genomic, and liquid biopsy data pave the way toward precision oncology and personalized therapeutic decision-making. Conclusions: Advances in imaging and theragnostics are reshaping prostate cancer management, bridging the gap between molecular biology and clinical practice to enable precision oncology.

Background/Objectives: Prostate cancer is the most prevalent malignancy in men and remains a leading cause of cancer-related mortality worldwide. Conventional imaging modalities exhibit limited sensitivity, particularly in the context of disease recurrence and advanced disease. Methods: A narrative review was conducted of studies published between 2015 and 2025, identified through PubMed, Embase, and Cochrane. Eligible publications addressed advanced imaging techniques, PSMA-targeted diagnostics and therapies, radiogenomics, liquid biopsy approaches, and artificial intelligence applica-tions and personalized medicine. Preclinical studies, single case reports, and conference abstracts without full text were excluded. Results: PSMA PET/CT outperforms conven-tional imaging for detection, and restaging, influencing clinical management across disease stages. Lutetium-177–PSMA-617 has become the standard radioligand therapy for metastatic castration-resistant prostate cancer, whereas alpha-emitting agents remain under clinical investigation. Radiogenomics and liquid biopsy assays (ctDNA, CTCs, AR-V7) provide complementary molecular insights. Artificial intelligence enhances imaging interpretations, standardization, and reproducibility, while multimodal data integration supports individualized risk stratification. Integrative models combining imaging, genomic, and liquid biopsy data pave the way toward precision oncology and personalized therapeutic decision-making. Conclusions: Advances in imaging and theragnostics are reshaping prostate cancer management, bridging the gap between molecular biology and clinical practice to enable precision oncology.

Urothelial carcinoma (UC) is a biologically and clinically heterogeneous disease arising from the bladder and upper urinary tract. Immune checkpoint inhibitors (ICIs) have transformed treatment of advanced UC, yet durable responses remain limited to a minority of patients, underscoring the need to better understand the tumor immune microenvironment (TME). This narrative review examines the UC immune phenotype across disease stages and anatomical sites, integrating evidence from bulk and single-cell transcriptomics, spatial profiling, and translational studies. We describe the principal immune cell populations of the UC TME—including cytotoxic and regulatory T cells, macrophages, myeloid-derived suppressor cells, natural killer cells, B cells, and dendritic cells—and their relationship to established molecular subtypes. We review how this immune landscape evolves from non-muscle-invasive to metastatic disease, including the distinct contexture of upper tract UC and variant histology. We critically evaluate established ICI biomarkers (PD-L1, FGFR3, tumor mutational burden, mismatch repair deficiency) alongside emerging candidates—tumor-infiltrating lymphocyte density, HLA class I expression, tertiary lymphoid structures, and multiparameter transcriptomic scores—noting that most remain investigational and require prospective validation before clinical use. Finally, we address key biological, technical, and clinical barriers to this research and outline future directions in AI-assisted digital pathology and multimodal biomarker integration. A comprehensive characterization of UC immune phenotype is essential to guide rational, biomarker-driven patient selection and optimize next-generation immunotherapy strategies.