Nonmuscle-invasive bladder cancer, old problems, new insights

Area of Science:

• Uro-oncology
• Pathology
• Oncological research

Background:

• Nonmuscle-invasive bladder cancer (NMIBC) constitutes approximately 75% of all bladder cancer diagnoses.
• Accurate pathological staging and grading are crucial for NMIBC patient management but remain challenging.
• Current diagnostic techniques for NMIBC exhibit variability and limitations.

Purpose of the Study:

• To review the existing challenges in NMIBC staging, grading, and treatment stratification.
• To discuss recent advancements impacting the pathological assessment of NMIBC.
• To explore the role of novel technologies, molecular insights, and artificial intelligence in NMIBC management.

Main Methods:

• Review of current literature on NMIBC staging and grading techniques.
• Analysis of recent findings in molecular classification and artificial intelligence applications for NMIBC.
• Evaluation of the prognostic relevance of substaging and its impact on treatment response prediction.

Main Results:

• Pathological staging and grading in NMIBC are complex, with diverse existing methodologies.
• Substaging demonstrates prognostic value and aids in predicting treatment response, particularly with Bacillus Calmette-Guérin (BCG) therapy.
• Emerging molecular classification and artificial intelligence (AI) approaches show significant promise for patient stratification.

Conclusions:

• Significant challenges persist in the diagnosis and management of NMIBC, necessitating further prospective research.
• New technologies, including molecular profiling and AI, are expected to enhance patient stratification and treatment strategies.
• Continued investigation into advanced diagnostic and prognostic tools is vital for improving outcomes in NMIBC patients.