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Multicenter Study
. 2024 May 21;24(1):65.
doi: 10.1186/s40644-024-00709-4.

MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study

Yanran Li #  1 , Yong Jin #  2 , Yunling Wang  1 , Wenya Liu  1 , Wenxiao Jia  1 , Jian Wang  3
Affiliations
Multicenter Study

MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study

Yanran Li et al. Cancer Imaging. .

Abstract

Objectives: Magnetic resonance (MR)-based radiomics features of brain metastases are utilised to predict epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) overexpression in adenocarcinoma, with the aim to identify the most predictive MR sequence.

Methods: A retrospective inclusion of 268 individuals with brain metastases from adenocarcinoma across two institutions was conducted. Utilising T1-weighted imaging (T1 contrast-enhanced [T1-CE]) and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequences, 1,409 radiomics features were extracted. These sequences were randomly divided into training and test sets at a 7:3 ratio. The selection of relevant features was done using the least absolute shrinkage selection operator, and the training cohort's support vector classifier model was employed to generate the predictive model. The performance of the radiomics features was evaluated using a separate test set.

Results: For contrast-enhanced T1-CE cohorts, the radiomics features based on 19 selected characteristics exhibited excellent discrimination. No significant differences in age, sex, and time to metastasis were observed between the groups with EGFR mutations or HER2 + and those with wild-type EGFR or HER2 (p > 0.05). Radiomics feature analysis for T1-CE revealed an area under the curve (AUC) of 0.98, classification accuracy of 0.93, sensitivity of 0.92, and specificity of 0.93 in the training cohort. In the test set, the AUC was 0.82. The 19 radiomics features for the T2-FLAIR sequence showed AUCs of 0.86 in the training set and 0.70 in the test set.

Conclusions: This study developed a T1-CE signature that could serve as a non-invasive adjunctive tool to determine the presence of EGFR mutations and HER2 + status in adenocarcinoma, aiding in the direction of treatment plans.

Clinical relevance statement: We propose radiomics features based on T1-CE brain MR sequences that are both evidence-based and non-invasive. These can be employed to guide clinical treatment planning in patients with brain metastases from adenocarcinoma.

Keywords: Adenocarcinoma; Brain metastasis; Gene expression; Radiomics; T1-CE brain MR sequences.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Workflow for radiomics modeling and analysis including segmentation of metastases and examples from MR images
Fig. 2
Fig. 2
The flow chart of patient enrollment
Fig. 3
Fig. 3
The MSE versus Lambda plot for T1-CE
Fig. 4
Fig. 4
The LASSO coefficients versus Lambda for T1-CE
Fig. 5
Fig. 5
ROC curves in the training and testing cohort for various MR sequences. A.T1-CE training cohort. B. T1-CE testing cohort. C. T2-FLAIR training cohort. D. T2-FLAIR testing cohort
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