Guest Editors
Prof. Dr. Liang Cheng
Email: liang_cheng@yahoo.com
Affiliation: The Warren Alpert Medical School, Brown University, Providence, USA
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Research Interests: genitourinary cancers; urologic oncology; molecular pathology; translational research; urologic surgical pathology; biomarkers
Prof. Qiwei Chen
Email: chenqiwei@dmu.edu.cn
Affiliation: Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian, China
Homepage:
Research Interests: artificial intelligence; pathological diagnosis; clinical management
Summary
Artificial intelligence is redefining the diagnostic mode for urinary system tumors, shifting from the traditional subjective image interpretation to objective and high-throughput computational analysis. By leveraging deep learning algorithms and image feature extraction techniques derived from multi-parameter magnetic resonance imaging and histopathological images, artificial intelligence offers unprecedented capabilities in disease diagnosis, reducing inter-observer differences, and improving diagnostic accuracy.
This special issue of CJU aims to explore the latest advancements in artificial intelligence-assisted diagnosis of urinary system tumors. We aim to bridge the gap between cutting-edge computational models and actual clinical applications, emphasizing how artificial intelligence can support urologists and radiologists in making more precise and personalized decisions.
Suggested Themes:
1. AI-Assisted Diagnosis of Prostate Cancer
2. Artificial Intelligence in Bladder Cancer Detection and Surveillance
3. Imaging-Based AI for Urologic Tumor Detection and Characterization
4. AI-Driven Risk Stratification in Urologic Cancers
5. Clinical Decision Support Systems in Precision Urology
6. Clinical Validation and Implementation of AI in Urologic Practice
This special issue focuses on the clinical translation of artificial intelligence in urologic cancers. We invite papers on six key topics: AI-assisted diagnosis of prostate cancer, AI in bladder cancer detection, imaging-based tumor characterization, AI-driven risk stratification, clinical decision support systems, and real-world validation of AI tools. Together, we are committed to shaping the future of precision diagnosis and treatment of urologic tumors.
Keywords
artificial intelligence; precision urology; multi-omics integration
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