Elsevier

NeuroImage: Clinical

Volume 17, 2018, Pages 306-311
NeuroImage: Clinical

MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

https://doi.org/10.1016/j.nicl.2017.10.030Get rights and content
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Highlights

  • We established a p53-related radiomic signature in lower-grade gliomas based on LASSO algorithm.

  • We developed a machine-learning model using the radiomic signature and a support vector machine.

  • P53 mutation status of lower-grade gliomas was predicted effectively based on our machine-learning model.

Abstract

Background

P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images.

Methods

Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training (n = 180) or validation (n = 92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets.

Results

The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features.

Conclusions

These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis.

Keywords

p53
Lower-grade gliomas
Radiogenomics
Prediction
Machine learning

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1

These authors contributed equally to this work.