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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.<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>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 (&gt; 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 &lt; 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSIONS<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3171\/2017.3.jns162479","type":"journal-article","created":{"date-parts":[[2017,10,20]],"date-time":"2017-10-20T13:59:51Z","timestamp":1508507991000},"page":"779-791","source":"Crossref","is-referenced-by-count":32,"title":["Machine learning applied to neuroimaging for diagnosis of adult classic Chiari malformation: role of the basion as a key morphometric indicator"],"prefix":"10.3171","volume":"129","author":[{"given":"Aintzane","family":"Urbizu","sequence":"first","affiliation":[{"name":"Conquer Chiari Research Center and"},{"name":"Pediatric Neurology Research Group,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryn A.","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of Biological Engineering, University of Idaho, Moscow, Idaho; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dulce","family":"Moncho","sequence":"additional","affiliation":[{"name":"Department of Clinical Neurophysiology,"},{"name":"Neurotraumatology and Neurosurgery Research Unit,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Rovira","sequence":"additional","affiliation":[{"name":"Magnetic Resonance Unit (IDI), Department of Radiology, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria A.","family":"Poca","sequence":"additional","affiliation":[{"name":"Neurotraumatology and Neurosurgery Research Unit,"},{"name":"Department of Neurosurgery, Vall d\u2019Hebron Research Institute, Vall d\u2019Hebron University Hospital, Universitat Aut\u00f2noma de Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Sahuquillo","sequence":"additional","affiliation":[{"name":"Neurotraumatology and Neurosurgery Research Unit,"},{"name":"Department of Neurosurgery, Vall d\u2019Hebron Research Institute, Vall d\u2019Hebron University Hospital, Universitat Aut\u00f2noma de Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfons","family":"Macaya","sequence":"additional","affiliation":[{"name":"Pediatric Neurology Research Group,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malena I.","family":"Espa\u00f1ol","sequence":"additional","affiliation":[{"name":"Department of Mathematics, The University of Akron, Ohio;"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"key":"b1","series-title":"J Comput Assist Tomogr","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1097\/00004728-198511000-00005","article-title":"Position of cerebellar tonsils in the normal population and in patients with Chiari malformation: a quantitative approach with MR imaging","volume":"9","author":"Aboulezz AO","year":"1985","unstructured":"Aboulezz AO, Sartor K, Geyer CA, Gado MH: Position of cerebellar tonsils in the normal population and in patients with Chiari malformation: a quantitative approach with MR imaging. 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