Guest Editors
Prof. Dr. Qiang Wei
Email: weiqiang339@126.com
Affiliation: Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
Homepage:
Research Interests: urologic diseases, multi-omics, artificial intelligence, precision medicine
Prof. Dehong Cao
Email: caodehong@scu.edu.cn
Affiliation: Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
Homepage:
Research Interests: prostatic disorders, disease diagnosis, prediction model, machine learning, artificial intelligence
Summary
Big data analytics is reshaping clinical urology, offering transformative tools to improve diagnostic accuracy, guide therapeutic decisions, and optimize patient outcomes. The integration of diverse, large-scale datasets—including genomics, medical imaging, electronic health records, and real-world clinical data—is unlocking new avenues for precision diagnosis, risk stratification, and individualized treatment in both benign and malignant urological diseases.
This Special Issue aims to highlight clinically oriented research that bridges advanced data science approaches with tangible applications in urological practice. We particularly welcome studies that incorporate artificial intelligence, machine learning, or predictive modeling with strong clinical validation—such as integration with surgical outcomes, pathology results, or treatment response data. Emphasis will be placed on work that demonstrates clear translational relevance and supports evidence-based decision-making in urology.
Topics of interest include, but are not limited to:
- Real-world data analysis to improve diagnostic or treatment outcomes in urology
- Multi-omics integration in prostate, bladder, kidney, or benign urological conditions
- Predictive modeling of treatment response or postoperative complications
- Precision medicine and individualized therapy supported by clinical datasets
- AI-assisted clinical decision-making in surgical and medical urology
- Biomarker discovery supported by large-scale data and clinical validation
- Outcome-based research using large-scale registries or hospital databases
- Risk stratification tools for urologic cancers and BPH in routine practice
By promoting research that translates data-driven insights into clinical utility, this Special Issue seeks to support innovations that are not only scientifically sound but also immediately relevant to patient care in urology.
Keywords
big data analytics, urological diseases, precision medicine, multi-omics integration, machine learning, personalized treatment, biomarker discovery
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