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A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma

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A Correction to this article was published on 09 March 2020

This article has been updated



To establish a quantitative MR model that uses clinically relevant features of tumor location and tumor volume to differentiate lower grade glioma (LRGG, grades II and III) and glioblastoma (GBM, grade IV).


We extracted tumor location and tumor volume (enhancing tumor, non-enhancing tumor, peritumor edema) features from 229 The Cancer Genome Atlas (TCGA)-LGG and TCGA-GBM cases. Through two sampling strategies, i.e., institution-based sampling and repeat random sampling (10 times, 70% training set vs 30% validation set), LASSO (least absolute shrinkage and selection operator) regression and nine–machine learning method–based models were established and evaluated.


Principal component analysis of 229 TCGA-LGG and TCGA-GBM cases suggested that the LRGG and GBM cases could be differentiated by extracted features. For nine machine learning methods, stack modeling and support vector machine achieved the highest performance (institution-based sampling validation set, AUC > 0.900, classifier accuracy > 0.790; repeat random sampling, average validation set AUC > 0.930, classifier accuracy > 0.850). For the LASSO method, regression model based on tumor frontal lobe percentage and enhancing and non-enhancing tumor volume achieved the highest performance (institution-based sampling validation set, AUC 0.909, classifier accuracy 0.830). The formula for the best performance of the LASSO model was established.


Computer-generated, clinically meaningful MRI features of tumor location and component volumes resulted in models with high performance (validation set AUC > 0.900, classifier accuracy > 0.790) to differentiate lower grade glioma and glioblastoma.

Key Points

• Lower grade glioma and glioblastoma have significant different location and component volume distributions.

• We built machine learning prediction models that could help accurately differentiate lower grade gliomas and GBM cases. We introduced a fast evaluation model for possible clinical differentiation and further analysis.

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Change history

  • 09 March 2020

    The original version of this article, published on 05 February 2020, unfortunately contained a mistake.



Area under ROC


Multimodal Brain Tumor Segmentation Challenge


Classification accuracy


Fluid-attenuated inversion recovery


Functional magnetic resonance imaging of the brain


FMRIB Software Library




Information gain


k-nearest neighbors algorithm


Least absolute shrinkage and selection operator


Low-grade glioma


Lower grade glioma


Montreal Neurological Institute


Neuroimaging Informatics Technology Initiative


Principal components


Principal component analysis


Random forest


Support vector machine


The Cancer Genome Atlas


The Cancer Imaging Archive


Peritumoral edema tumor volume


Enhancing tumor volume


Non-enhancing tumor volume


Tumor core volume


Whole tumor volume


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The results here are in whole or part based upon data generated by the TCGA Research Network:


The authors state that this work has not received any funding.

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Authors and Affiliations


Corresponding author

Correspondence to Robert K. Fulbright.

Ethics declarations


The scientific guarantor of this publication is Dr. Robert Fulbright.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was not required because only deidentified data is used in this study.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported. The TCGA-LGG and TCGA-GBM cohorts were involved in this study. Due to the enormous publishing scale, here we are not able to list or include every associated study. However, the cohort resource and include/exclude criteria have been introduced and cited in detail.

The major differences between the cohort used in our study and other study are as follows:

1. In our study, only TCGA-LGG and TCGA-GBM cases with MRI images available on TCIA were included.

2. The abovementioned cases were subsequently selected by segmentation data availability; only cases that have GLISTRboost-generated and manually checked segmentation data were selected.


• retrospective

• diagnostic study

• multicenter study

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The original version of this article was revised: The name of E. Zeynep Erson-Omay was presented incorrectly.

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Cao, H., Erson-Omay, E.Z., Li, X. et al. A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma. Eur Radiol 30, 3073–3082 (2020).

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  • Glioma
  • Neoplasm grading
  • Tumor burden
  • Machine learning