BACKGROUND AND PURPOSE: The diagnosis of Chiari malformation type I (CMI) relies on MRI identification of a tonsillar descent (TD) through the foramen magnum, reflecting the overcrowding of an underdeveloped posterior cranial fossa (PCF). However, TD occurs in some patients with normal-sized PCF and, conversely, some patients with borderline or no TD have small PCF. We thus sought to identify a set of prototypic PCF measures for the diagnosis of CMI. METHODS: We performed nineteen measurements of the PCF on sagittal MRI of 100 cases with cerebellar TD ≥5 mm and 50 control individuals, compared the average values in both cohorts and used logistic regression to devise a probability model to predict CMI status. RESULTS: Significant decrements were detected for several PCF-related measures in the patients' cohort. We developed a probability model that combined seven of these parameters to predict diagnosis with 93% sensitivity and 92% specificity. CONCLUSIONS: The addition of simple morphometric measurements in the diagnostic work-up of patients with suspected CMI may facilitate radiological diagnosis. Moreover, identification of the subset of CMI that arise from basichondrocranium underdevelopment is important for both, selection of the most appropriate therapeutic approach as well as proper CMI categorization in research studies.
BACKGROUND AND PURPOSE: The diagnosis of Chiari malformation type I (CMI) relies on MRI identification of a tonsillar descent (TD) through the foramen magnum, reflecting the overcrowding of an underdeveloped posterior cranial fossa (PCF). However, TD occurs in some patients with normal-sized PCF and, conversely, some patients with borderline or no TD have small PCF. We thus sought to identify a set of prototypic PCF measures for the diagnosis of CMI. METHODS: We performed nineteen measurements of the PCF on sagittal MRI of 100 cases with cerebellar TD ≥5 mm and 50 control individuals, compared the average values in both cohorts and used logistic regression to devise a probability model to predict CMI status. RESULTS: Significant decrements were detected for several PCF-related measures in the patients' cohort. We developed a probability model that combined seven of these parameters to predict diagnosis with 93% sensitivity and 92% specificity. CONCLUSIONS: The addition of simple morphometric measurements in the diagnostic work-up of patients with suspected CMI may facilitate radiological diagnosis. Moreover, identification of the subset of CMI that arise from basichondrocranium underdevelopment is important for both, selection of the most appropriate therapeutic approach as well as proper CMI categorization in research studies.
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Authors: Enver I Bogdanov; Aisylu T Faizutdinova; Elena G Mendelevich; Alexey S Sozinov; John D Heiss Journal: Neurosurgery Date: 2019-05-01 Impact factor: 4.654
Authors: James R Houston; Natalie J Allen; Maggie S Eppelheimer; Jayapalli Rajiv Bapuraj; Dipankar Biswas; Philip A Allen; Sarel J Vorster; Mark G Luciano; Francis Loth Journal: Neuroradiol J Date: 2019-06-18
Authors: Bryn A Martin; Theresia I Yiallourou; Soroush Heidari Pahlavian; Suraj Thyagaraj; Alexander C Bunck; Francis Loth; Daniel B Sheffer; Jan Robert Kröger; Nikolaos Stergiopulos Journal: Ann Biomed Eng Date: 2015-10-07 Impact factor: 3.934