Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients.
Aging (Albany NY)
; 12(21): 21481-21503, 2020 11 05.
Article
em En
| MEDLINE
| ID: mdl-33159021
ABSTRACT
Approximately 30% of medulloblastoma (MB) patients exhibit metastasis at initial diagnosis, which often leads to a poor prognosis. Here, by using univariate Cox regression analysis, two machine learning methods (Lasso-penalized Cox regression and random survival forest-variable hunting (RSF-VH)), and multivariate Cox regression analysis, we established two metastasis-related prognostic models, including the 47-mRNA-based model based on the Lasso method and the 21-mRNA-based model based on the RSF-VH method. In terms of the results of the receiver operating characteristic (ROC) curve analyses, we selected the 47-mRNA metastasis-associated model with the higher area under the curve (AUC). The 47-mRNA-based prognostic model could classify MB patients into two subgroups with different prognoses. The ROC analyses also suggested that the 47-mRNA metastasis-associated model may have a better predictive ability than MB subgroup. Multivariable Cox regression analysis demonstrated that the 47-mRNA-based model was independent of other clinical characteristics. In addition, a nomogram comprising the 47-mRNA-based model was built. The results of ROC analyses suggested that the nomogram had good discrimination ability. Our 47-mRNA metastasis-related prognostic model and nomogram might be an efficient and valuable tool for overall survival (OS) prediction and provide information for individualized treatment decisions in patients with MB.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Biomarcadores Tumorais
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Neoplasias Cerebelares
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Técnicas de Apoio para a Decisão
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Perfilação da Expressão Gênica
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Nomogramas
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Transcriptoma
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Aprendizado de Máquina
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Meduloblastoma
Tipo de estudo:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adolescent
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Adult
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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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Infant
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Male
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Middle aged
Idioma:
En
Revista:
Aging (Albany NY)
Assunto da revista:
GERIATRIA
Ano de publicação:
2020
Tipo de documento:
Article