ArticleBrain communications2025
Stratification of surgical outcomes in trigeminal neuralgia using multimodal data.
Article in Brain communications, 2025. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic pain remains a challenge for clinicians, with limited individualized predictive tools that can aid with diagnosis, disease course, or prediction of treatment outcomes. We hypothesized that a comprehensive analysis, encompassing a patient's complete pain-related clinical data, medical history and brain imaging, can identify key contributors linked to surgical outcomes and stratify specific outcome categories for trigeminal neuralgia (TN)-chronic facial pain syndrome. Using supervised and unsupervised machine learning approaches, we analysed data from 102 subjects with classical TN. Pre-surgical clinical data were processed through unsupervised learning to delineate key clinical contributors of TN outcome stratification and their correlation with surgical response. Concurrently, we applied supervised learning to pre-surgical T1-weighted brain magnetic resonance imaging. Clinical data analysis uncovered pain and non-pain-related measures-including pain frequency, degree of medication relief, pain character, presence of diabetes and cancer history-as the most significant in forecasting surgical outcome. Analysis revealed strong correlation of pre-surgical clinical data with surgical response duration (
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