Dataset Information

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

ABSTRACT:

Background

We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI.

Methods

61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation.

Results

We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set.

Conclusions

The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity.

Trial registration

This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

SUBMITTER: Liu Z

PROVIDER: S-EPMC10496393 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

Liu Zhen Z Hong Xuanke X Wang Linglong L Ma Zeyu Z Guan Fangzhan F Wang Weiwei W Qiu Yuning Y Zhang Xueping X Duan Wenchao W Wang Minkai M Sun Chen C Zhao Yuanshen Y Duan Jingxian J Sun Qiuchang Q Liu Lin L Ding Lei L Ji Yuchen Y Yan Dongming D Liu Xianzhi X Cheng Jingliang J Zhang Zhenyu Z Li Zhi-Cheng ZC Yan Jing J

BMC cancer 20230911 1

<h4>Background</h4>We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI.<h4>Methods</h4>61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups o ...[more]

PMID: 37697238

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Dataset Information

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

Background

Methods

Results

Conclusions

Trial registration

Publications

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

Similar Datasets

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