Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.
Eur Radiol
; 30(7): 3834-3842, 2020 Jul.
Article
em En
| MEDLINE
| ID: mdl-32162004
ABSTRACT
BACKGROUND AND PURPOSE:
Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and IDH status. MATERIALS ANDMETHODS:
Radiomic features (n = 250) were extracted from preoperative MRI data of 296 lower grade glioma patients from databases at our institutional (n = 205) and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (n = 91) datasets. For predicting overall survival, random survival forest models were trained with radiomic features; non-imaging prognostic factors including age, resection extent, WHO grade, and IDH status on the institutional dataset, and validated on the TCGA/TCIA dataset. The performance of the random survival forest (RSF) model and incremental value of radiomic features were assessed by time-dependent receiver operating characteristics.RESULTS:
The radiomics RSF model identified 71 radiomic features to predict overall survival, which were successfully validated on TCGA/TCIA dataset (iAUC, 0.620; 95% CI, 0.501-0.756). Relative to the RSF model from the non-imaging prognostic parameters, the addition of radiomic features significantly improved the overall survival prediction accuracy of the random survival forest model (iAUC, 0.627 vs. 0.709; difference, 0.097; 95% CI, 0.003-0.209).CONCLUSION:
Radiomic phenotyping with machine learning can improve survival prediction over clinical profile and genomic data for lower grade gliomas. KEY POINTS ⢠Radiomics analysis with machine learning can improve survival prediction over the non-imaging factors (clinical and molecular profiles) for lower grade gliomas, across different institutions.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias Encefálicas
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Imageamento por Ressonância Magnética
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Aprendizado de Máquina
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Glioma
Tipo de estudo:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article