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Machine learning applied to neuroimaging for diagnosis of adult classic Chiari malformation: role of the basion as a key morphometric indicator.
J Neurosurg. 2018 09; 129(3):779-791.JN

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.

Authors+Show Affiliations

1Conquer Chiari Research Center and. 2Pediatric Neurology Research Group.3Department of Biological Engineering, University of Idaho, Moscow, Idaho; and.4Department of Clinical Neurophysiology. 5Neurotraumatology and Neurosurgery Research Unit.6Magnetic Resonance Unit (IDI), Department of Radiology, and.5Neurotraumatology and Neurosurgery Research Unit. 7Department of Neurosurgery, Vall d'Hebron Research Institute, Vall d'Hebron University Hospital, Universitat Autònoma de Barcelona, Spain.5Neurotraumatology and Neurosurgery Research Unit. 7Department of Neurosurgery, Vall d'Hebron Research Institute, Vall d'Hebron University Hospital, Universitat Autònoma de Barcelona, Spain.2Pediatric Neurology Research Group.8Department of Mathematics, The University of Akron, Ohio.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

29053075

Citation

Urbizu, Aintzane, et al. "Machine Learning Applied to Neuroimaging for Diagnosis of Adult Classic Chiari Malformation: Role of the Basion as a Key Morphometric Indicator." Journal of Neurosurgery, vol. 129, no. 3, 2018, pp. 779-791.
Urbizu A, Martin BA, Moncho D, et al. Machine learning applied to neuroimaging for diagnosis of adult classic Chiari malformation: role of the basion as a key morphometric indicator. J Neurosurg. 2018;129(3):779-791.
Urbizu, A., Martin, B. A., Moncho, D., Rovira, A., Poca, M. A., Sahuquillo, J., Macaya, A., & Español, M. I. (2018). Machine learning applied to neuroimaging for diagnosis of adult classic Chiari malformation: role of the basion as a key morphometric indicator. Journal of Neurosurgery, 129(3), 779-791. https://doi.org/10.3171/2017.3.JNS162479
Urbizu A, et al. Machine Learning Applied to Neuroimaging for Diagnosis of Adult Classic Chiari Malformation: Role of the Basion as a Key Morphometric Indicator. J Neurosurg. 2018;129(3):779-791. PubMed PMID: 29053075.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Machine learning applied to neuroimaging for diagnosis of adult classic Chiari malformation: role of the basion as a key morphometric indicator. AU - Urbizu,Aintzane, AU - Martin,Bryn A, AU - Moncho,Dulce, AU - Rovira,Alex, AU - Poca,Maria A, AU - Sahuquillo,Juan, AU - Macaya,Alfons, AU - Español,Malena I, Y1 - 2017/10/20/ PY - 2017/10/21/pubmed PY - 2019/10/24/medline PY - 2017/10/21/entrez KW - CM = Chiari malformation KW - Chiari malformation KW - DT = decision tree KW - FM = foramen magnum KW - LR = logistic regression KW - MRI KW - NB = naïve Bayes KW - PCF = posterior cranial fossa KW - SVM = support vector machine KW - TH = tonsillar herniation KW - basion KW - k-NN = k-nearest neighbors KW - machine learning KW - skull base SP - 779 EP - 791 JF - Journal of neurosurgery JO - J Neurosurg VL - 129 IS - 3 N2 - 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. SN - 1933-0693 UR - https://www.unboundmedicine.com/medline/citation/29053075/Machine_learning_applied_to_neuroimaging_for_diagnosis_of_adult_classic_Chiari_malformation:_role_of_the_basion_as_a_key_morphometric_indicator_ L2 - https://thejns.org/doi/10.3171/2017.3.JNS162479 DB - PRIME DP - Unbound Medicine ER -