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Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients.


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.

SUBMITTER: Chen K 

PROVIDER: S-EPMC7695392 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients.

Chen Kui K   Huang Bingsong B   Yan Shan S   Xu Siyi S   Li Keqin K   Zhang Kuiming K   Wang Qi Q   Zhuang Zhongwei Z   Wei Liang L   Zhang Yanfei Y   Liu Min M   Lian Hao H   Zhong Chunlong C  

Aging 20201105 21


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 RS  ...[more]

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