Dr. Roger Li, a genitourinary oncologist at Moffitt Cancer Center, has addressed the risk of disease progression in patients with low-grade non-muscle-invasive bladder cancer (NMIBC) and the emerging role of artificial intelligence (AI) in enhancing risk stratification. His insights reveal critical nuances about the clinical implications of progression and highlight the potential of AI tools in pathology assessment.
While low-grade NMIBC is typically associated with favorable oncologic outcomes, Dr. Li pointed out that progression can occur along a spectrum, with varying clinical implications depending on whether the progression leads to high-grade, muscle-invasive, or metastatic disease. He emphasized that true progression from low-grade NMIBC to muscle-invasive or metastatic bladder cancer is relatively rare, occurring in fewer than 5% of patients.
Dr. Li explained that in large patient cohorts, only a small number experience progression to muscle-invasive disease. Nonetheless, he noted that this rarity does not diminish the significance of the more frequent progression from low-grade to high-grade disease. He estimated that between 10% and 20% of patients with low-grade NMIBC may advance to high-grade disease, underscoring the need for careful monitoring.
Grading discrepancies among pathologists can complicate risk assessments, which is where AI-based pathology tools emerge as a promising solution. These AI models, trained on digitized hematoxylin and eosin (H&E) slides, offer a practical approach that utilizes standard pathology images available in routine clinical practice. Unlike genomic assays, AI tools do not require specialized sequencing platforms, making them accessible across various clinical settings, including community urology practices.
The capabilities of AI extend beyond human analysis. Dr. Li highlighted that AI platforms can examine nuclear and cellular features at a scale that surpasses human capability. By analyzing thousands of morphologic parameters, AI could uncover patterns linked to clinically significant outcomes, such as the likelihood of progression to high-grade disease. This granularity allows AI to detect biological signals that can refine prognostication and improve early risk identification.
If validated through prospective studies, AI-driven pathology assessment could enable more personalized surveillance and treatment strategies for patients with low-grade NMIBC. Those identified as having high-risk morphologic signatures might benefit from intensified monitoring or earlier therapeutic interventions. In contrast, patients with lower-risk profiles could potentially avoid unnecessary procedures and overtreatment.
Dr. Li concluded that AI-enabled pathology holds the promise of enhancing clinical decision-making by providing objective, reproducible, and widely deployable risk stratification tools for patients with NMIBC. As the healthcare landscape evolves, the integration of AI in pathology may transform the management approach for bladder cancer, ultimately improving patient outcomes.
