A multi-modal approach for decision making in bladder cancer.

Area of Science:

• Oncology
• Medical Diagnostics
• Health Technology

Background:

• Bladder cancer presents significant global health challenges, including diagnostic difficulties, high costs, and frequent recurrence.
• Current methods like cystoscopy and histopathology have limitations in detecting flat lesions, staging accuracy, and consistency.

Purpose of the Study:

• To review recent advancements in bladder cancer diagnostics and treatment.
• To highlight the potential of emerging technologies in improving patient care and outcomes.

Main Methods:

• Review of recent developments in artificial intelligence (AI), blue-light cystoscopy, narrow-band imaging, cytology, and urinary markers for early detection.
• Analysis of multiparametric MRI, radiomics, genomics, and AI in histopathology for improved staging and risk stratification.

Main Results:

• AI, advanced imaging, and molecular techniques show promise for enhanced early detection and diagnosis of bladder cancer.
• These technologies significantly improve staging, risk stratification, and personalized therapy selection, aiding prognostication.

Conclusions:

• Despite progress, challenges in standardization, validation, cost, and ethics need addressing for clinical implementation.
• A multimodal strategy integrating these advances is crucial for advancing precision oncology in bladder cancer and reducing healthcare burdens.