Artificial intelligence-based prediction of stone-free status in patients undergoing mini-percutaneous nephrolithotomy without retrograde insertion of a ureteral catheter.

Yunxun Liu, Wen Hu, Peiyu He, Songlin Liang, Junjie Ai, Guohua Zeng, Mingyong Li

Abstract readObservational Study

In one paragraph

Observational study in World journal of urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yunxun LiuUrology department, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.

Wen HuUrology department, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.

Peiyu HeUrology department, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.

Songlin LiangUrology department, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.

Junjie AiUrology department, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.

Guohua ZengUrology department, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China. gzgyzgh@vip.sina.com.

Mingyong LiUrology department, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China. myli1123@126.com.

Funding

Clinical Research Project of University of South China USCKF201902K01Natural Science Foundation of Hunan Province 2025JJ70119Teaching Reform Research Project for Postgraduates of South China University 2023JG036Teaching Reform Research Project of Regular Undergraduate Colleges and Universities in Hunan Province 202502000762Teaching Reform Research Project of South China University 2023XJG11

6 · The paper itself

Abstract

purposeThis study aimed to develop and validate an artificial intelligence (AI) predictive model for postoperative stone-free status (SFS) specifically in patients undergoing mini-percutaneous nephrolithotomy without retrograde insertion of a ureteral catheter (mPCNL-nRUC).

methodsThis single-center prospective observational study included 181 patients with upper urinary tract stones who underwent mPCNL-nRUC between March 2019 and March 2025. Preoperative clinical, laboratory, and imaging data were collected. Five machine learning (ML) algorithms were employed to construct SFS prediction models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with Guy's stone score and S.T.O.N.E. nephrolithometry system. The SHapley Additive exPlanations (SHAP) method was used for interpretability analysis of the optimal model.

resultsAll surgeries successfully established percutaneous renal access without intraoperative conversion to retrograde ureteral catheter insertion. The immediate postoperative stone-free rate (SFR) was 72.93% (132/181). The overall complication rate was 24.31%, with the majority being Clavien‑Dindo grade I or II. Among the five models, the support vector machine (SVM) model demonstrated the best predictive performance with AUC of 0.875, significantly outperforming Guy's stone score (AUC = 0.659, p < 0.001) and S.T.O.N.E. nephrolithometry system (AUC = 0.770, p = 0.005). The best-performing SVM model has been deployed on an openly accessible online platform to facilitate clinical application. SHAP analysis revealed that stone burden, preoperative serum creatinine, preoperative white blood cell count, stone complexity, and hydronephrosis were the most critical predictors of SFS.

conclusionmPCNL-nRUC is a safe, effective, and simplified procedure in selected patients. The interpretable AI model provides a powerful tool for clinical individualized decision-making.

Indexed as

Artificial IntelligenceKidney CalculiNephrolithotomy, PercutaneousUreteral CalculiAdultFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsProspective StudiesUrinary CathetersArtificial intelligenceMachine learningPercutaneous nephrolithotomyStone free statusUreteral catheter

Identifiers

PMID42665740

DOI10.1007/s00345-026-06728-z

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.