Machine learning-based prediction of stone-free rate after retrograde intrarenal surgery for lower pole renal stones
ML-based models, particularly LightGBM, offer robust predictive capability for SFR following RIRS in patients with LPS. These tools may enhance preoperative planning and personalized surgical strategies. Future prospective studies are warranted to further validate their clinical utility and expand on feature integration.
Authors
- Vineet Gauhar (ORCID: https://orcid.org/0000-0002-3740-7141)
- Sung Yong Cho (ORCID: https://orcid.org/0000-0001-9271-6951)
- Yen‐Chun Wang (ORCID: https://orcid.org/0000-0003-3569-1510)
- Hsiang‐Ying Lee (ORCID: https://orcid.org/0000-0003-2405-4874)
- Yu-Hung Tung
- Jose Carlo Elises (ORCID: https://orcid.org/0000-0003-2911-2869)
Institutions
- Kaohsiung Medical University (TW)
- Ng Teng Fong General Hospital (SG)
- Kaohsiung Medical University Chung-Ho Memorial Hospital (TW)
- Seoul National University Hospital (KR)
- Asian Institute of Gastroenterology (IN)
Publication Details
- Journal
- World Journal of Urology
- Published
- 2025-07-12
- DOI
- https://doi.org/10.1007/s00345-025-05762-7
- Citations
- 1
- Primary Topic
- Kidney Stones and Urolithiasis Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Seoul National University Hospital
- Kaohsiung Medical University
- Kaohsiung Medical University Chung-Ho Memorial Hospital
- National Science and Technology Council