Peritumoural MRI radiomics signature of brain metastases can predict epidermal growth factor receptor mutation status in lung adenocarcinoma

Abstract

•Brain metastases' MRI radiomics predicts lung adenocarcinoma's EGFR mutation.•Fusion model with gender not superior to FLAIR radiomics model (VOIperitumor 1mm).•Radiomics score is a key independent predictor of EGFR mutations. AIMTo investigate whether magnetic resonance imaging (MRI) radiomics features of brain metastases (BMs) can predict epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma.MATERIALS AND METHODSBetween June 2014 and December 2022, 58 histopathologically confirmed lung adenocarcinoma patients (27 with EGFR wild-type, 31 with EGFR mutation) who underwent gadobenate dimeglumine-enhanced brain MRI were recruited retrospectively. A total of 123 metastatic brain lesions were allocated randomly into the training cohort (n=86) and test cohort (n=37) at a ratio of 7:3. Radiomics models based on multi-sequence MRI images in different regions such as volume of interest (VOI)enhancing tumour, VOIwholetumour, VOIperitumour 1mm, VOIperitumour 3mm, and VOIperitumour 5mm were built. The optimal radiomics model was integrated into the clinical or radiological indicators to construct a fusion model through multivariable logistic regression analysis.RESULTSThe optimal radiomics model based on the VOIperitumour 1mm, a combination of nine features selected from the fluid-attenuated inversion recovery (FLAIR) sequence, yielded areas under the curves (AUCs) of >0.75 in the training and test cohorts. The prediction of the fusion model with integration of clinical factors (age) and radiomics score (the optimal radiomics model) was not better than that of the optimal radiomics model alone in the test cohort (AUC: 0.808 and 0.785, respectively, p=0.525).CONCLUSIONThe FLAIR radiomics model based on VOIperitumour 1mm as an effective biomarker helps predict EGFR mutation status in lung adenocarcinoma patients with BMs and then assists clinicians in selecting optimal treatment strategies. To investigate whether magnetic resonance imaging (MRI) radiomics features of brain metastases (BMs) can predict epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Between June 2014 and December 2022, 58 histopathologically confirmed lung adenocarcinoma patients (27 with EGFR wild-type, 31 with EGFR mutation) who underwent gadobenate dimeglumine-enhanced brain MRI were recruited retrospectively. A total of 123 metastatic brain lesions were allocated randomly into the training cohort (n=86) and test cohort (n=37) at a ratio of 7:3. Radiomics models based on multi-sequence MRI images in different regions such as volume of interest (VOI)enhancing tumour, VOIwholetumour, VOIperitumour 1mm, VOIperitumour 3mm, and VOIperitumour 5mm were built. The optimal radiomics model was integrated into the clinical or radiological indicators to construct a fusion model through multivariable logistic regression analysis. The optimal radiomics model based on the VOIperitumour 1mm, a combination of nine features selected from the fluid-attenuated inversion recovery (FLAIR) sequence, yielded areas under the curves (AUCs) of >0.75 in the training and test cohorts. The prediction of the fusion model with integration of clinical factors (age) and radiomics score (the optimal radiomics model) was not better than that of the optimal radiomics model alone in the test cohort (AUC: 0.808 and 0.785, respectively, p=0.525). The FLAIR radiomics model based on VOIperitumour 1mm as an effective biomarker helps predict EGFR mutation status in lung adenocarcinoma patients with BMs and then assists clinicians in selecting optimal treatment strategies.

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