MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma.
AJNR Am J Neuroradiol
; 40(1): 154-161, 2019 01.
Article
em En
| MEDLINE
| ID: mdl-30523141
ABSTRACT
BACKGROUND AND PURPOSE:
Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma. MATERIALS ANDMETHODS:
In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance.RESULTS:
Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma.CONCLUSIONS:
This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
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Tipos_de_cancer
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Outros_tipos
Base de dados:
MEDLINE
Assunto principal:
Imageamento por Ressonância Magnética
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Neoplasias Cerebelares
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Meduloblastoma
Tipo de estudo:
Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adolescent
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Child
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Child, preschool
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Female
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Humans
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Male
Idioma:
En
Revista:
AJNR Am J Neuroradiol
Ano de publicação:
2019
Tipo de documento:
Article