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Prognostic Effect of Trigeminal Neuralgia Treated With Percutaneous Balloon Compression by Machine Learning-based

Ji Wu1, Keyu Chen1, Hao Mei2

  • 1Department of Neurosurgery, Wuhan University Zhongnan Hospital, Wuhan, People's Republic of China.

Pain Physician
|December 17, 2024
PubMed
Summary

Machine learning combined with imaging nomograms can predict trigeminal neuralgia (TN) recurrence after percutaneous balloon compression. This approach shows promise for improving patient outcomes and guiding clinical decisions for TN management.

Keywords:
nomogrampercutaneous balloon compressionprognosistrigeminal neuralgiaMachine learning

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Machine Learning

Background:

  • Trigeminal neuralgia (TN) significantly impacts quality of life.
  • Percutaneous balloon compression is a common TN treatment, but recurrence rates remain a concern.

Purpose of the Study:

  • To develop a machine learning-based clinical imaging nomogram to predict TN recurrence after percutaneous balloon compression.

Main Methods:

  • Retrospective analysis of 209 TN patients treated with percutaneous balloon compression.
  • Extraction of 16 morphological imaging features using 3D slicer software.
  • Development and validation of machine learning models (Random Forest, SVM, GLM, XGBoost) using clinical and imaging data.

Main Results:

  • A predictive model incorporating gender, affected side, and MajorAxisLength demonstrated high accuracy (AUC 0.99).
  • Machine learning models achieved high predictive performance, with AUCs ranging from 0.986 to 0.993.
  • The validated model showed good predictive ability in an independent cohort (AUC 0.857).

Conclusions:

  • Machine learning combined with a clinical imaging nomogram effectively predicts TN recurrence post-percutaneous balloon compression.
  • The developed nomogram is suitable for clinical application in managing TN patients.
  • Further prospective and multi-center studies are warranted to validate these findings.