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Radiomics signature of brain metastasis: prediction of EGFR mutation status

European Radiology volume 31, pages 4538–4547 (2021)Cite this article

Abstract

Objectives

To predict epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma using MR-based radiomics signature of brain metastasis and explore the optimal MR sequence for prediction.

Methods

Data from 52 patients with brain metastasis from lung adenocarcinoma (28 with mutant EGFR, 24 with wild-type EGFR) were retrospectively reviewed. Contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR), T2WI, and DWI sequences were selected for radiomics features extraction. A total of 438 radiomics features were extracted from each MR sequence. All sequences were randomly divided into training and validation cohorts. The least absolute shrinkage selection operator was used to select informative features, a radiomics signature was built with the logistic regression model of the training cohort, and the radiomics signature performance was evaluated using the validation cohort and an independent testing data set.

Results

The radiomics signature built on 9 selected features showed good discrimination in both the training and validation cohorts for T2-FLAIR. The radiomics signature of T2-FLAIR yielded an AUC of 0.987, a classification accuracy of 0.991, sensitivity of 1.000, and specificity of 0.980 in the validation cohort. The AUC was 0.871 in the independent testing data set. The AUCs of our radiomics signature to differentiate exon 19 and exon 21 mutations were 0.529, 0.580, 0.645, and 0.406 for T1-CE, T2-FLAIR, T2WI, and DWI, respectively.

Conclusions

We developed a T2-FLAIR radiomics signature that can be used as a noninvasive auxiliary tool for predicting EGFR mutation status in lung adenocarcinoma, which is helpful to guide therapeutic strategies.

Key Points

• MR-based radiomics signature of brain metastasis may help predict EGFR mutation status in lung adenocarcinoma, especially using T2-FLAIR.

• Nine radiomics features extracted from T2-FLAIR sequence strongly correlate with EGFR mutation status.

• Radiomics features reflect tumor heterogeneity through potential changes in tissue morphology caused by EGFR mutation.

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Abbreviations

ACC:

Classification accuracy

ARMS-PCR:

Amplification refractory mutation system–polymerase chain reaction

ctDNA:

Circulating tumor DNA

EGFR:

Epidermal growth factor receptor

FOV:

Field of view

KPS:

Karnofsky Scores

LASSO:

Least absolute shrinkage selection operator

NGS:

Next-generation sequencing technology

PFS:

Progression-free survival

SEN:

Sensitivity

SPE:

Specificity

T1-CE:

Contrast-enhanced T1-weighted imaging

T2-FLAIR:

T2 fluid-attenuated inversion recovery

TE:

Echo time

TKI:

Tyrosine kinase inhibitor

TR:

Repetition time

VEGF:

Vascular endothelial growth factor

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Funding

This study has received funding by the National Natural Science Foundation of China (ID: 81670046) and State Key Laboratory of Computer Architecture (ICT, CAS) (ID: CARCHA202002).

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Author notes

  1. Guangyu Wang and Bomin Wang contributed equally to this work.

Affiliations

  1. Cancer Therapy and Research Center, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, People’s Republic of China

    Guangyu Wang & Mingyong Han

  2. School of Information Science and Engineering, Shandong University, 27 Shanda South Road, Jinan, 250100, Shandong, People’s Republic of China

    Bomin Wang & Zhi Liu

  3. Medical Imaging Department, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, People’s Republic of China

    Zhou Wang & Jianjun Xiu

  4. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University and Healthcare Big Data Institute of Shandong University, Jinan, 250012, People’s Republic of China

    Wenchao Li

Authors
  1. Guangyu Wang
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  2. Bomin Wang
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  3. Zhou Wang
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  4. Wenchao Li
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  5. Jianjun Xiu
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  6. Zhi Liu
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  7. Mingyong Han
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Corresponding authors

Correspondence to Zhi Liu or Mingyong Han.

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The scientific guarantor of this publication is Mingyong Han.

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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.

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No complex statistical methods were necessary for this paper.

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• performed at one institution

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Wang, G., Wang, B., Wang, Z. et al. Radiomics signature of brain metastasis: prediction of EGFR mutation status. Eur Radiol 31, 4538–4547 (2021). https://doi.org/10.1007/s00330-020-07614-x

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Keywords

  • Magnetic resonance imaging
  • Adenocarcinoma of lung
  • Neoplasm metastases
  • Logistic models