Topic: Biomarkers

Utilizing ctDNA in Bladder Cancer

Utilizing ctDNA in Bladder Cancer

by | Nov 2024

Leonard G. Gomella, MD, FACS, explores the potential of circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) as biomarkers in cancer detection, treatment monitoring, and prognosis.

In this 12-minute presentation, Dr. Gomella reviews recent studies, such as those conducted at the European Association of Urology (EAU), demonstrating ctDNA’s potential for early detection, risk assessment, treatment response evaluation, and post-treatment surveillance in bladder cancer. Trials such as InVigor010 highlight ctDNA’s prognostic utility, where analysis revealed that ctDNA positivity predicted a higher risk of relapse.

Gomella reports that innovations extend to ctDNA detection in urine, offering a non-invasive method to monitor disease burden. These developments and the integration of advanced sequencing technologies mark a new era in cancer management, facilitating earlier interventions and more effective treatment planning.

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Imaging and Biomarkers in Patient Selection for Focal Ablation

Imaging and Biomarkers in Patient Selection for Focal Ablation

by | Aug 2024

Ezequiel Becher, MD, discusses the critical role of imaging and biomarkers in selecting patients for focal ablation in prostate cancer treatment.

He highlights the advancements in imaging technologies that have revolutionized prostate cancer diagnostics, particularly multiparametric MRI (mpMRI). In addition to imaging, Dr. Becher underscores the significance of biomarkers in patient selection. While prostate-specific antigen (PSA) levels are traditionally used, they are complemented by more specific biomarkers such as PCA3, TMPRSS2-ERG, and genomic tests that provide a deeper understanding of tumor biology and aggressiveness.

Dr. Becher also addresses the challenges and limitations of current imaging and biomarker techniques. He advocates for continuous refinement of these technologies and the development of standardized protocols to enhance diagnostic accuracy. Looking forward, he points to the potential of artificial intelligence and machine learning in improving diagnostic precision and patient selection processes.

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