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Predicting long-term outcomes in patients with classical trigeminal neuralgia following microvascular decompression with an MRI-based radiomics nomogram: a multicentre study. | LitMetric

AI Article Synopsis

  • This study focused on creating a clinical-radiomics nomogram to predict long-term outcomes for patients with classical trigeminal neuralgia (CTN) after microvascular decompression (MVD).
  • Researchers analyzed data from 455 patients, extracting 2030 radiomics features, and identified 16 key features used to develop the nomogram through advanced statistical methods.
  • The nomogram showed strong predictive ability with high concordance indices, successfully differentiating patients into high-risk and low-risk groups for adverse outcomes after MVD.

Article Abstract

Objectives: This study aimed to develop a clinical-radiomics nomogram to predict the long-term outcomes of patients with classical trigeminal neuralgia (CTN) following microvascular decompression (MVD).

Materials And Methods: This retrospective study included 455 patients with CTN who underwent MVD from three independent institutions A total of 2030 radiomics features from the cistern segment of the trigeminal nerve were extracted computationally from the three-dimensional steady-state free precession and three-dimensional time-of-flight magnetic resonance angiography sequences. Using the least absolute shrinkage and selection operator regression, 16 features were chosen to develop radiomics signatures. A clinical-radiomics nomogram was subsequently developed in the development cohort of 279 patients via multivariate Cox regression. The predictive performance and clinical application of the nomogram were assessed in an external cohort consisting of 176 patients.

Results: Sixteen highly outcome-related radiomics features extracted from multisequence images were used to construct the radiomics model, with concordance indices (C-index) of 0.804 and 0.796 in the development and test cohorts, respectively. Additionally, a clinical-radiomics nomogram was developed by incorporating both radiomics features and clinical characteristics (i.e., pain type and degree of neurovascular compression) and yielded higher C-indices of 0.865 and 0.834 in the development and test cohorts, respectively. K‒M survival analysis indicated that the nomogram successfully stratified patients with CTN into high-risk and low-risk groups for poor outcomes (hazard ratio: 37.18, p < 0.001).

Conclusion: Our study findings indicated that the clinical-radiomics nomogram exhibited promising performance in accurately predicting long-term pain outcomes following MVD.

Clinical Relevance Statement: This model had the potential to aid clinicians in making well-informed decisions regarding the treatment of patients with CTN.

Key Points: Trigeminal neuralgia recurs in about one-third of patients after undergoing MVD. The clinical-radiomics nomogram stratified patients into high- and low-risk groups for poor surgical outcomes. Using this nomogram could better inform patients of recurrence risk and allow for discussion of alternative treatments.

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Source
http://dx.doi.org/10.1007/s00330-024-10775-8DOI Listing

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