AI and ctDNA: Advancing Precision Bladder Cancer Care

The landscape of bladder cancer treatment is shifting toward a more personalized approach as clinicians integrate circulating tumor DNA (ctDNA) and artificial intelligence (AI) into standard workflows.

During the OneOncology APP Symposium, Rian J. Dickstein, MD, FACS, highlighted how these advancements are refining prognostic accuracy and treatment selection, offering significant implications for oncology nursing practice.

The role of biomarkers: ctDNA and utDNA

A primary focus of the discussion was the utility of ctDNA as both a predictive and prognostic tool. Dickstein emphasized that understanding these biomarkers is essential for the care team, as they provide a clearer picture of a patient's trajectory.

"ctDNA is both predictive and prognostic, so we know that ctDNA positivity pretty much always predicts an adverse outcome, where ctDNA-negative patients do better," Dickstein stated.

For oncology nurses, serial ctDNA measurements provide a valuable monitoring tool to track disease progression or response to therapy. Dickstein noted that the predictive nature of the test allows clinicians to escalate or de-escalate care based on the patient's status.

"If you convert from negative to positive, or if you have persistently positive disease, you're more likely to respond to a therapy," he explained. Conversely, for patients who test negative, the technology offers a pathway to "potentially avoid the overtoxicity of adjuvant therapy," which is a critical consideration for maintaining patient quality of life.

Looking ahead, Dickstein expressed optimism regarding urinary tumor DNA (utDNA), describing it as a potential "game changer." This emerging biomarker could help determine if non-muscle invasive disease has been adequately treated and may eventually guide decisions regarding bladder-sparing therapies for patients with muscle-invasive disease.

AI integration and the BCG shortage

The symposium also addressed the integration of AI, which is currently impacting care through enhanced imaging and pathologic algorithms. In the diagnostic setting, AI is being used to increase the sensitivity of cystoscopies by helping clinicians detect tumors more accurately.

Perhaps most relevant to current clinical challenges is the use of pathologic AI algorithms to navigate the ongoing BCG shortage. Recent trials have explored the ability of AI to predict which patients with non-muscle invasive bladder cancer will respond to BCG treatment. By identifying responders with higher precision, the oncology team can ensure that the limited supply of BCG is directed to those most likely to benefit.

"I think that's really going to help us in terms of getting the right treatment to the right patient, number one, and in terms of our ability to save our supply of BCG in the world of our BCG shortage currently," Dickstein concluded.

For oncology nurses and advanced practice providers, these technological shifts mean a more data-driven approach to patient education and symptom management, ensuring that therapeutic interventions are both timely and necessary.