Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers.
Neoplasia
; 36: 100869, 2023 Feb.
Article
em En
| MEDLINE
| ID: mdl-36566592
ABSTRACT
INTRODUCTION:
Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes.METHODS:
Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes.RESULTS:
K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p < 0.05) as well as the gene expression of BRAF (p<0.05). It was also found that age (p < 0.05), tumor location (p < 0.01), and tumor histology (p < 0.0001) differed significantly between the imaging subtypes.CONCLUSION:
In this exploratory work, it was found that clustering of pLGGs based on radiomic features identifies distinct, imaging-based subtypes that correlate with important molecular markers and demographic details. This finding supports the notion that incorporation of radiomic data could augment our ability to better characterize pLGGs.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
/
Tipos_de_cancer
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Outros_tipos
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Encefálicas
/
Glioma
Tipo de estudo:
Prognostic_studies
Limite:
Child
/
Humans
Idioma:
En
Revista:
Neoplasia
Assunto da revista:
NEOPLASIAS
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos