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

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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.

ABBREVIATIONS CM = Chiari malformation; DT = decision tree; FM = foramen magnum; k-NN = k-nearest neighbors; LR = logistic regression; NB = naïve Bayes; PCF = posterior cranial fossa; SVM = support vector machine; TH = tonsillar herniation.

Article Information

Correspondence Aintzane Urbizu, Duke Molecular Physiology Institute, Duke University School of Medicine, 300 N Duke St., Durham, NC 27701. email: aintzane.urbizu.serrano@duke.edu.

INCLUDE WHEN CITING Published online October 20, 2017; DOI: 10.3171/2017.3.JNS162479.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

© AANS, except where prohibited by US copyright law.

Headings

Figures

  • View in gallery

    Representative sagittal T1-weighted MR images corresponding to each of the analyzed cohorts: controls (A), CM-I (B and C), CM-0 (D), CM-I with moderate TH (E), and CM-1.5 (F). Numbers indicate the measurements performed in all individuals: 1 indicates tentorium length; 2, supraoccipital length; 3, anteroposterior diameter of FM; 4, clivus length; 5, PCF area; 6, osseous PCF area; 7, tentorium angle; 8, height PCF; 9, width of PCF; 10, basilar impression; 11, distance from pons to FM; 12, distance from corpus callosum to FM; 13, distance from fastigium to FM; 14, Wackenheim angle; and 15, basal angle. Panels B and C are modified from Urbizu A, Poca MA, Vidal X, Rovira A, Sahuquillo J, Macaya A: MRI-based morphometric analysis of posterior cranial fossa in the diagnosis of Chiari malformation type I. J Neuroimaging 24:250–256, 2014. Used with permission. Figure is available in color online only.

  • View in gallery

    Plots showing analyses of correlation between the clivus length and the 5 measurements most relevant according to the 7 supervised classification methods—the distance from the pons, the corpus callosum (CC), and the fastigium to the FM; the basal angle; and the Wackenheim angle (A–E, respectively)—and between the distance from the pons to the FM and the basal angle (F). Blue dots indicate measurements obtained in controls (n = 50), and green dots those obtained in patients with CM-I (n = 117). The respective correlation coefficients and p values are shown for each analysis. Figure is available in color online only.

  • View in gallery

    Ordination plots depicting the distribution of data points used for training (left) and validation (right). The classification line generated by the support vector machine using only 2 features is also shown. Figure is available in color online only.

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