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10.1007/s40122-025-00796-6

http://scihub22266oqcxt.onion/10.1007/s40122-025-00796-6
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suck abstract from ncbi

pmid41353704      Pain+Ther 2025 ; ? (?): ?
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  • Prediction of Postherpetic Neuralgia in Patients with Acute and Subacute Herpetic Neuralgia Using Structural Magnetic Resonance Imaging: A Retrospective Study #MMPMID41353704
  • Wu D; Peng B; Hua Y; Geng W; Huang B; Lu S; Chen J; He K; Wang Y; Rao Q; Jiang Z; Wang C; Dai Y; Ji J; Zhao Z
  • Pain Ther 2025[Dec]; ? (?): ? PMID41353704show ga
  • INTRODUCTION: This study integrated structural magnetic resonance imaging (sMRI) of the brain with clinical characteristics to identify the "vulnerable brain regions" and risk factors associated with the development of postherpetic neuralgia (PHN) in patients with acute and subacute herpetic neuralgia. Furthermore, the study explored the combined predictive performance of these neuroimaging and clinical indicators. METHODS: From February 2023 to January 2025, a total of 214 hospitalized patients with acute and subacute herpetic neuralgia were enrolled. Follow-up was conducted via telephone or outpatient visits, revealing that 116 patients (54.98%) developed PHN, while 95 did not. Clinical data and magnetic resonance imaging (MRI) data were collected for all participants. T1-weighted structural MRI images underwent preprocessing procedures including N4 bias field correction, skull stripping, brain tissue segmentation, and parcellation. Gray matter volume (GMV) values were extracted from 90 predefined regions of interest (ROIs) for further analysis. Group differences were assessed using two-tailed Student's t-tests or the non-parametric Kruskal-Wallis H test, as appropriate. Features showing significant intergroup differences in GMV were integrated with clinical variables to train machine learning models, and receiver-operating characteristic (ROC) curve analysis was employed to evaluate their predictive performance for PHN. RESULTS: Significant differences were observed between the PHN and Non-PHN groups in several clinical variables, including age, body mass index (BMI), age >/= 50 years, disease duration, admission Numeric Rating Scale (NRS) score, hospitalization during the acute phase (< 1 month), involved dermatome, and total Charlson Comorbidity Index (CCI) score (all P < 0.05). In terms of neuroimaging findings, GMV differed significantly between the two groups in the following brain regions: the left inferior frontal gyrus (triangular part), fusiform gyrus, Heschl's gyrus, and superior temporal gyrus; the right orbital part of the inferior frontal gyrus, lentiform nucleus (globus pallidus); and the bilateral cingulate gyrus, hippocampus, and caudate nucleus (P < 0.05, corrected for multiple comparisons using the false discovery rate [FDR] method). The combined model integrating T1-weighted MRI features and clinical characteristics achieved an area under the ROC curve (AUC) of 0.748 (95% CI 0.677-0.816) for predicting the occurrence of PHN. CONCLUSION: This study is the first to innovatively integrate sMRI to identify "vulnerable brain regions" associated with the transition from acute and subacute herpetic neuralgia to PHN. By combining GMV metrics with clinical features, the study provides a novel approach for predicting the development of PHN.
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