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21.03.2018 | Computer Applications

Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study

verfasst von: Zhi-Cheng Li, Hongmin Bai, Qiuchang Sun, Qihua Li, Lei Liu, Yan Zou, Yinsheng Chen, Chaofeng Liang, Hairong Zheng

Erschienen in: European Radiology | Ausgabe 9/2018

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Abstract

Objectives

To build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O⁶-methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM).

Methods

In this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors.

Results

The radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis.

Conclusions

Radiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features.

Key Points

• Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts.

• All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features.

• Combing clinical factors with radiomics features did not benefit the prediction performance.

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Titel: Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study
verfasst von: Zhi-Cheng Li
Hongmin Bai
Qiuchang Sun
Qihua Li
Lei Liu
Yan Zou
Yinsheng Chen
Chaofeng Liang
Hairong Zheng
Publikationsdatum: 21.03.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 9/2018
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-017-5302-1

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Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study