Machine learning applied to neuroimaging for diagnosis of adult classic Chiari malformation: role of the basion as a key morphometric indicator

Abstract

OBJECTIVE The current diagnostic criterion for Chiari malformation Type I (CM-I), based on tonsillar herniation (TH), includes a diversity of patients with amygdalar descent that may be caused by a variety of factors. In contrast, patients presenting with an overcrowded posterior cranial fossa, a key characteristic of the disease, may remain misdiagnosed if they have little or no TH. The objective of the present study was to use machine-learning classification methods to identify morphometric measures that help discern patients with classic CM-I to improve diagnosis and treatment and provide insight into the etiology of the disease. METHODS Fifteen morphometric measurements of the posterior cranial fossa were performed on midsagittal T1-weighted MR images obtained in 195 adult patients diagnosed with CM. Seven different machine-learning classification methods were applied to images from 117 patients with classic CM-I and 50 controls matched by age and sex to identify the best classifiers discriminating the 2 cohorts with the minimum number of parameters. These classifiers were then tested using independent CM cohorts representing different entities of the disease. RESULTS Machine learning identified combinations of 2 and 3 morphometric measurements that were able to discern not only classic CM-I (with more than 5 mm TH) but also other entities such as classic CM-I with moderate TH and CM Type 1.5 (CM-1.5), with high accuracy (> 87%) and independent of the TH criterion. In contrast, lower accuracy was obtained in patients with CM Type 0. The distances from the lower aspect of the corpus callosum, pons, and fastigium to the foramen magnum and the basal and Wackenheim angles were identified as the most relevant morphometric traits to differentiate these patients. The stronger significance (p < 0.01) of the correlations with the clivus length, compared with the supraoccipital length, suggests that these 5 relevant traits would be affected more by the relative position of the basion than the opisthion. CONCLUSIONS Tonsillar herniation as a unique criterion is insufficient for radiographic diagnosis of CM-I, which can be improved by considering the basion position. The position of the basion was altered in different entities of CM, including classic CM-I, classic CM-I with moderate TH, and CM-1.5. The authors propose a predictive model based on 3 parameters, all related to the basion location, to discern classic CM-I with 90% accuracy and suggest considering the anterior alterations in the evaluation of surgical procedures and outcomes.

Cerebellar tonsil ectopia measurement in type I Chiari malformation patients show poor inter-operator reliability

“…Based on comprehensive review of the literature, at least 50% of patients that come to medical doctors for CM-I assessment are considered asymptomatic [10, 12, 14, 17, 19, 24, 30, 33, 39, 40]. Increasing use of MRI has revealed that large tonsillar descent can be accompanied by no symptoms and vice versa [2, 15, 41].…”

“…Thus, CM-I prevalence is upwards of two orders of magnitude less than the radiological findings of tonsillar descent [19]. It is clear that additional diagnostic measures or diagnostic methods such as machine learning are needed to accurately detect CM-I and thereby aid in the decision of treatment options [40, 44]. In addition to static morphometric analysis, there is a need for dynamic MRI-based methods that identify symptomatic CM-I and correspond to severity of symptoms.…”

Multi-parameter-based Radiological Diagnosis of Chiari Malformation using Machine Learning Technology

“…A reported study by Alperin et al identified four measures that distinguished CM-I patients from a healthy cohort with 97 percent sensitivity and 100 percent specificity (4). Another study produced a probability predictor based on a logistic regression (LR) model that took into account seven PCF measures and had a sensitivity of 93% and specificity of 92% in distinguishing patients with classic CM-I from those with a standard PCF (16).…”

“…. , b n are parameters of the model, which are calculated from the training stage with maximum likelihood technique (16,17).…”

“…However, recent data also shows the association of such malformation with smaller posterior cranial fossa (PCF) volume and the anatomical issues regarding the Odontoid (4-7). Recent studies suggest more radiological criteria that may aid to the diagnosis of CM (5,6,7,16,18,21).…”