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Objective <p>This study aims to both develop and evaluate a predictive model for ureteral access sheath(UAS)placement success using preoperative CT-based 3D ureteral imaging and machine learning techniques. Specifically, it investigates the impact of ureteral anatomical angles on UAS placement success and integrates these angles with multiple machine learning models for preoperative risk stratification. The study also assesses the performance of these models, providing insights into their predictive accuracy and clinical applicability.</p> Methods <p>We retrospectively analyzed 302 patients who underwent initial flexible ureteroscopy lithotripsy (FURS) from January 2022 to August 2023 at Xiangya Hospital, Zhuzhou. None had preoperative ureteral stents. Preoperative CT scans were used to reconstruct the lower ureter in 3D and measure key anatomical angles. Logistic regression identified independent predictors of UAS placement success. Eight machine learning models were developed, with SHAP analysis applied to assess each variable’s contribution to prediction accuracy.</p> Results <p>The UAS placement success rate was 71.19%. Univariate analysis found that both the angle between the ureteral orifice and body axis (∠α; OR = 0.94, 95% CI: 0.89–0.99, <i>p</i> = 0.019) and the angle between the outermost segment of the lower ureter and body axis (∠β; OR = 0.93, 95% CI: 0.89–0.97, <i>p</i> &lt; 0.001) were significantly associated with success. Multivariate analysis confirmed ∠β as an independent predictor (OR = 0.95, 95% CI: 0.90–0.99, <i>p</i> = 0.024). SHAP analysis highlighted ∠β as the most influential variable, with failure risk rising sharply when ∠β exceeded 40°.</p> Conclusion <p>The ∠β is a critical independent factor affecting UAS placement success. Integrating 3D CT measurements with machine learning allows quantitative risk assessment, aiding in preoperative planning and personalized surgical decision-making. This approach shows strong potential for clinical application.</p>

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Preoperative CT imaging and machine learning models for predicting ureteral access sheath placement success in non-stented patients with ureteral calculi: a retrospective cohort study

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Xipeng Wu,
  • Cheng Chen,
  • Wenda Zou,
  • Rongli Ding,
  • Ziwei Liu

摘要

Objective

This study aims to both develop and evaluate a predictive model for ureteral access sheath(UAS)placement success using preoperative CT-based 3D ureteral imaging and machine learning techniques. Specifically, it investigates the impact of ureteral anatomical angles on UAS placement success and integrates these angles with multiple machine learning models for preoperative risk stratification. The study also assesses the performance of these models, providing insights into their predictive accuracy and clinical applicability.

Methods

We retrospectively analyzed 302 patients who underwent initial flexible ureteroscopy lithotripsy (FURS) from January 2022 to August 2023 at Xiangya Hospital, Zhuzhou. None had preoperative ureteral stents. Preoperative CT scans were used to reconstruct the lower ureter in 3D and measure key anatomical angles. Logistic regression identified independent predictors of UAS placement success. Eight machine learning models were developed, with SHAP analysis applied to assess each variable’s contribution to prediction accuracy.

Results

The UAS placement success rate was 71.19%. Univariate analysis found that both the angle between the ureteral orifice and body axis (∠α; OR = 0.94, 95% CI: 0.89–0.99, p = 0.019) and the angle between the outermost segment of the lower ureter and body axis (∠β; OR = 0.93, 95% CI: 0.89–0.97, p < 0.001) were significantly associated with success. Multivariate analysis confirmed ∠β as an independent predictor (OR = 0.95, 95% CI: 0.90–0.99, p = 0.024). SHAP analysis highlighted ∠β as the most influential variable, with failure risk rising sharply when ∠β exceeded 40°.

Conclusion

The ∠β is a critical independent factor affecting UAS placement success. Integrating 3D CT measurements with machine learning allows quantitative risk assessment, aiding in preoperative planning and personalized surgical decision-making. This approach shows strong potential for clinical application.