ArticleNeurosurgical review2024
A nomogram based on radiomics and clinical information to predict prognosis in percutaneous balloon compression for the treatment of trigeminal neuralgia.
Article in Neurosurgical review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Association of compression pressure and SCN8A and SCN9A gene polymorphisms with the recurrence of balloon compression-treated trigeminal neuralgia.Neurosurgical review · 2025Article
- Letter to the editor: A nomogram based on radiomics and clinical information to predict prognosis in percutaneous balloon compression for the treatment of trigeminal neuralgia.Neurosurgical review · 2024Article
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12 authors.
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Abstract
objectiveTo develop a clinical-radiomics nomogram based on clinical information and radiomics features to predict the prognosis of percutaneous balloon compression (PBC) for the treatment of trigeminal neuralgia (TN).
methodsThe retrospective study involved clinical data from 149 TN patients undergoing PBC at Zhongnan Hospital, Wuhan University from January 2018 to January 2022. The free open-source software 3D Slicer was used to extract all radiomic features from the intraoperative X-ray balloon region. The relationship between clinical information and TN prognosis was analyzed by univariate logistic analysis and multivariate logistic analysis. Using R software, the optimal radiomics features were selected using the least absolute shrinkage and selection operator (Lasso) algorithm. A prediction model was constructed based on the clinical information and radiomic features, and a nomogram was visualized. The performance of the clinical radiomics nomogram in predicting the prognosis of PBC in TN treatment was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).
resultsA total of 149 patients were eventually included. The clinical factors influencing the prognosis of TN in univariate analysis were compression severity score and TN type. The lasso algorithm Max-Relevance and Min-Redundancy(mRMR) was used to select two predictors from 13 morphology-related radiomics features, including elongation and surface-volume ratio. A total of 4 predictors were used to construct a prediction model and nomogram. The AUC was 0.886(95% confidence interval (CI), 0.75 to 0.96), indicating that the model's good predictive ability. DCA demonstrated the nomogram's high clinical applicability.
conclusionClinical-radiomics nomogram constructed by combining clinical information and morphology-related radiomics features have good potential in predicting the prognosis of TN for PBC treatment. However, this needs to be further studied and validated in several independent external patient populations.
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