Urinary bladder cancer (UBC) is a prevalent malignancy worldwide, exhibiting high recurrence rates and significant morbidity and mortality. Traditional diagnostic and prognostic methods often fall short in providing the precision required for effective patient stratification and personalized treatment. Genomic and transcriptomic studies have revolutionized our understanding of UBC by unveiling molecular alterations that drive tumor initiation, progression, and therapeutic response. This systematic review explores the role and application of genomic and transcriptomic analyses in the diagnostics and survival prediction of non-invasive and invasive UBC. We conducted a comprehensive literature search in MEDLINE, Web of Science, and Scopus up to October 2023, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search yielded 1,256 records (412 in MEDLINE, 378 in Web of Science, and 466 in Scopus), and 356 duplicates were removed. Our findings highlight key mechanisms of action, including mutations in FGFR3, TP53, and RB1 genes, and alterations in pathways such as PI3K/AKT/mTOR and MAPK/ERK, which are pivotal in UBC pathogenesis. Recent research advances, including liquid biopsies and single-cell sequencing, offer promising non-invasive diagnostic tools and deeper insights into tumor heterogeneity. This review underscores the critical importance of integrating genomic and transcriptomic data into clinical practice to improve diagnostics, prognostic assessments, and personalized treatment strategies for UBC patients. Future research should focus on integrating multi-omics data and validating molecular biomarkers in large clinical trials to further enhance patient outcomes.

Background: Prostate cancer is a leading cause of cancer-related morbidity and mortality among men worldwide. Fusion biopsy, combining magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS) guidance, has enhanced the detection of clinically significant prostate cancer. However, challenges such as inter-operator variability and accurate lesion targeting persist. Artificial intelligence (AI) and machine learning (ML) offer potential improvements in diagnostic accuracy and efficiency. Objective: To systematically review the role and perspectives of AI and ML in improving the efficiency of fusion biopsy in men with prostate cancer. Materials and Methods: Following PRISMA guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus up to October 2023. Studies assessing the application of AI and ML in fusion biopsy for prostate cancer were included. Results: A total of 1,236 records were identified (MEDLINE: 432; Web of Science: 398; Scopus: 406), with 312 duplicates removed. Titles and abstracts of 924 articles were screened, and 68 qualified for full-text eligibility assessment. Twenty-seven articles met the inclusion criteria and were qualitatively synthesized. Conclusion: AI and ML hold promise in improving the efficiency and accuracy of fusion biopsies in prostate cancer. Large-scale, prospective studies and standardized protocols are necessary to validate these technologies and facilitate their integration into clinical practice.