ArticleEuropean radiology2022
Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- CT-based radiomics for predicting stone-free rate after ESWL: a systematic review and meta-analysis.World journal of urology · 2025Pooled it
- Use of artificial intelligence for sepsis risk prediction after flexible ureteroscopy: a systematic review.Revista do Colegio Brasileiro de Cirurgioes · 2023Pooled it
- Development of a machine learning model for predicting urosepsis after ureteroscopic lithotripsy.Urolithiasis · 2026Article
- Preoperative machine learning algorithm for predicting urosepsis after percutaneous nephrolithotomy using EMR data.Urolithiasis · 2025Article
- An interpretable machine learning model integrating computed tomography radiomics and clinical features for predicting the urosepsis after percutaneous nephrolithotomy.Biomedical engineering online · 2025Article
- Development and validation of an explainable machine learning model for predicting sepsis risk following flexible ureteroscopic lithotripsy.Urolithiasis · 2025Article
- Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.Journal of endourology · 2025Article
- Latent Profile of Pain Catastrophizing and Its Relationship to Emotional State in Patients with Ureteral Calculi: A Cross-Sectional.Journal of pain research · 2025Article
- Applications of flexible ureteroscope lithotripsy with negative pressure suction and percutaneous nephrolithotomy in the treatment of 2-3 cm diameter renal calculi.Clinical and experimental nephrology · 2025Article
- Efficacy and cost-effectiveness analysis of flexible ureteroscopic lithotripsy with TFDS in the treatment of urolithiasis.Frontiers in surgery · 2024Article
- Relationship between stone volume, average CT value and operation time and efficiency before ureteral soft lens laser lithotripsy.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review.Asian journal of urology · 2023Review
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
objectivesTo explore the utility of radiomics and deep learning model in assessing the risk factors for sepsis after flexible ureteroscopy lithotripsy (FURL) or percutaneous nephrolithotomy (PCNL) in patients with ureteral calculi.
methodsThis retrospective analysis included 847 patients with treatment-naive proximal ureteral calculi who received FURL or PCNL. All participants were preoperatively conducted non-contrast computed tomography scans, and relevant clinical information was meanwhile collected. After propensity score matching, the radiomics model was established to predict the onset of sepsis. A deep learning model was also adapted to further improve the prediction accuracy. Performance of these trained models was verified in another independent external validation set including 40 cases of ureteral calculi patients.
resultsThe overall incidence of sepsis after FURL or PCNL was 5.9%. The least absolute shrinkage and selection operator (LASSO) regression analysis revealed 26 predictive variables, with an overall AUC of 0.881 (95% CI, 0.813-0.931) and an AUC of 0.783 (95% CI, 0.766-0.801) in external validation cohort. Judicious adaption of a deep neural network (DNN) model to our dataset improved the AUC to 0.920 (95% CI, 0.906-0.933) in the internal validation. To eliminate the overfitting, external validation was carried out for DNN model (AUC = 0.874 (95% CI, 0.858-0.891)).
conclusionsThe DNN was more effective than the LASSO model in revealing risk factors for sepsis after FURL or PCNL in single ureteral calculi patients, and females are more susceptible to sepsis than males. Deep learning models have the potential to act as gatekeepers to facilitate patient stratification. KEY POINTS: • Both the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models were shown to be effective in sepsis prediction. • The DNN model achieved superior prediction capability, with an AUC of 0.920 (95% CI, 0.906-0.933). • DNN-assisted model has potential to serve as a gatekeeper to facilitate patient stratification.
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