Artificial Intelligence and Big Data in Urological Oncology: From Radiomics to Real-World Evidence

Area of Science:

• Urological Oncology
• Artificial Intelligence
• Big Data Analytics

Background:

• Artificial intelligence (AI) and big data are transforming urological oncology.
• Enhancing diagnostic precision, prognostic assessment, and treatment personalization for prostate, bladder, and kidney cancers.

Purpose of the Study:

• To review the application and impact of AI and big data in urological oncology.
• To assess the diagnostic and prognostic capabilities of AI models in common urological malignancies.

Main Methods:

• Systematic search of PubMed and MEDLINE up to September 2025.
• Inclusion of English-language, peer-reviewed human studies.
• Keywords: artificial intelligence, deep learning, radiomics, real-world evidence, urological oncology.

Main Results:

• AI-driven radiomics and deep learning models show high accuracy (AUCs 0.80-0.95) in detecting and characterizing urological malignancies using various imaging modalities (MRI, CT, PET) and histopathology.
• High diagnostic performance for lesion detection, staging, and risk stratification.
• Accurate mutation prediction (85%-95%) in renal cancer and high sensitivity/specificity (>90%) in cystoscopic image analysis.

Conclusions:

• AI and big data are integrating diagnostic imaging, pathology, and clinical practice in urological oncology.
• Continued integration promises precise, equitable, and adaptive cancer care.
• Challenges include limited external validation and generalizability; future progress requires multicenter standardization and federated learning.