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A machine learning approach using stone volume to predict stone-free status at ureteroscopy

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Abstract

Introduction

To develop a predictive model incorporating stone volume along with other clinical and radiological factors to predict stone-free (SF) status at ureteroscopy (URS).

Material and methods

Retrospective analysis of patients undergoing URS for kidney stone disease at our institution from 2012 to 2021. SF status was defined as stone fragments < 2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments > 2 mm at XR KUB or US KUB at 3 months follow up. We specifically included all non-SF patients to optimise our algorithm for identifying instances with residual stone burden. SF patients were also randomly sampled over the same time period to ensure a more balanced dataset for ML prediction. Stone volumes were measured using preprocedural CT and combined with 19 other clinical and radiological factors. A bagged trees machine learning model with cross-validation was used for this analysis.

Results

330 patients were included (SF: n = 276, not SF: n = 54, mean age 59.5 ± 16.1 years). A fivefold cross validated RUSboosted trees model has an accuracy of 74.5% and AUC of 0.82. The model sensitivity and specificity were 75% and 72.2% respectively. Variable importance analysis identified total stone volume (17.7% of total importance), operation time (14.3%), age (12.9%) and stone composition (10.9%) as important factors in predicting non-SF patients. Single and cumulative stone size which are commonly used in current practice to guide management, only represented 9.4% and 4.7% of total importance, respectively.

Conclusion

Machine learning can be used to predict patients that will be SF at the time of URS. Total stone volume appears to be more important than stone size in predicting SF status. Our findings could be used to optimise patient counselling and highlight an increasing role of stone volume to guide endourological practice and future guidelines.

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Data availability

Data is confidential and not avaiable for general requests.

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Funding

Funding from the Institute for Life Sciences (University of Southampton).

Author information

Author notes

  1. Ganesh Vigneswaran and Ren Teh have equally contributed to this work.

Authors and Affiliations

  1. Department of Interventional Radiology, University Hospital Southampton, Southampton, UK

    Ganesh Vigneswaran, Ren Teh & Sachin Modi

  2. Cancer Sciences, University of Southampton, Southampton, UK

    Ganesh Vigneswaran

  3. Department of Urology, University Hospital Southampton, Tremona Road, Southampton, UK

    Francesco Ripa, Amelia Pietropaolo & Bhaskar Kumar Somani

  4. Electronics and Computer Science, University of Southampton, Southampton, UK

    Jagmohan Chauhan

Authors
  1. Ganesh Vigneswaran
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  2. Ren Teh
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  5. Sachin Modi
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  7. Bhaskar Kumar Somani
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Contributions

G. Vigneswaran—Formal analysis and writing—Original draft preparation. R Teh—Data collection, analysis, review & editing. F Ripa—Data collection, editing. A Pietropao—Data collection. S Modi—Review & editing. J Chauhan—Review & editing. B K Somani—Conceptualization, methodology and review & editing.

Corresponding author

Correspondence to Bhaskar Kumar Somani.

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Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

The research protocols performed in this study complied with the ethical principles of the Declaration of Helsinki. This retrospective study was approved by HRA, IRAS 324872 (Ethical approval number).

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All patients gave their consent for this study.

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Vigneswaran, G., Teh, R., Ripa, F. et al. A machine learning approach using stone volume to predict stone-free status at ureteroscopy. World J Urol 42, 344 (2024). https://doi.org/10.1007/s00345-024-05054-6

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