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Interventional Ultrasound

Development of a dose prediction model for ultrasound-guided thrombin treatment of pseudoaneurysms using the LightGBM algorithm

  • Xinyue Wang 1 ,
  • Ya Yuan 1 ,
  • Hua Shu 1 ,
  • Kunpeng Cao 1 ,
  • Xinhua Ye 1 ,
  • Lu Li , 1,
Expand
  • 1.Department of Ultrasound, the First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China

Received date: 2024-12-30

  Online published: 2025-04-01

Copyright

Copyright by Chinese Medical Association No content published by the journals of Chinese Medical Association may be reproduced or abridged without authorization. Please do not use or copy the layout and design of the journals without permission. All articles published represent the opinions of the authors, and do not reflect the official policy of the Chinese Medical Association or the Editorial Board, unless this is clearly specified.

Abstract

Objective

To develop a dose prediction model for ultrasound-guided thrombin treatment of pseudoaneurysms using the Light Gradient Boosting Machine (LightGBM) algorithm.

Methods

A retrospective analysis was conducted on 84 patients diagnosed with femoral artery pseudoaneurysms via ultrasound and treated with ultrasound-guided thrombin injection at the First Affiliated Hospital of Nanjing Medical University between January 2018 and December 2024.Patients were categorized into three groups based on thrombin dosage:low-dose (<500 IU, 30 cases), mediumdose (≥500 IU and <1000 IU, 36 cases), and high-dose (≥1000 IU, 18 cases).The cohort was randomly divided into a training set (67 cases) and a validation set (17 cases) at an 8:2 ratio.Feature variables were screened using ordinal logistic regression analysis to construct a logistic-based thrombin dose prediction model.Additionally, LightGBM contribution-based feature selection was applied to build a LightGBM-based dose prediction model.Model performance was evaluated using overall accuracy,micro-average area under the curve (AUC), recall, F1-score, and receiver operating characteristic(ROC) curve analysis.

Results

In the training set, the logistic regression model demonstrated an overall accuracy of 0.677 and a micro-average AUC of 0.744 (95% confidence interval [CI]:0.674-0.815); in the validation set, the corresponding values were 0.686 and 0.758 (95%CI:0.624-0.891).The LightGBM-based model exhibited superior performance, with a training set overall accuracy of 0.930,micro-average AUC of 0.975 (95%CI:0.955-0.995), micro-average recall of 92.5%, and micro-average F1-score of 0.899.In the validation set, it achieved an overall accuracy of 0.804, micro-average AUC of 0.872 (95%CI:0.766-0.978), micro-average recall of 76.5%, and micro-average F1-score of 0.722.

Conclusion

The LightGBM-based thrombin dose prediction model effectively forecasts thrombin dosage requirements, offering a valuable reference for achieving precision and individualized treatment in ultrasound-guided thrombin injection therapy.

Cite this article

Xinyue Wang , Ya Yuan , Hua Shu , Kunpeng Cao , Xinhua Ye , Lu Li . Development of a dose prediction model for ultrasound-guided thrombin treatment of pseudoaneurysms using the LightGBM algorithm[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2025 , 22(02) : 153 -161 . DOI: 10.3877/cma.j.issn.1672-6448.2025.02.009

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