Rationale and Objectives
Timely identifying T790M mutation for non-small cell lung cancer (NSCLC) patients
with brain metastases (BM) is essential to adjust targeted treatment strategies. To
develop and validate radiomics models based on multisequence MRI for differentiating
patients with T790M resistance from no T790M mutation in BM and explore the optimal
sequence for prediction.
Materials and Methods
This retrospective study enrolled 233 patients with proven of BM in NSCLC which included
95 with T790M and 138 without T790M from two hospitals as the training cohort and
testing cohort separately. Radiomics features extracted from T2WI, T2 fluid-attenuated
inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced
T1-weighted imaging (T1-CE) sequence respectively. The most predictable features were
selected based on the maximal information coefficient and Boruta method. Then four
radiomics models were built to characterize T790M mutation by random forest classifier.
ROC curves, F1 score and DCA curves were constructed to validate the capability and
verify the performance of four models.
Results
The DWI model showed best performance with AUC and F1 score of 0.886 and 0.789 in
the training cohort, 0.850 and 0.743 in the testing cohort. DCA curves also showed
higher overall net benefit from the DWI model than from the remaining three models
in the testing cohort. Other three models also had some classification power whether
in the training or testing cohort, especially T2-FLAIR model.
Conclusion
Multisequence MRI-based radiomics has potential to predict the emergence of EGFR T790M
resistance mutations especially the radiomics signature based on DWI sequence.
Key Words
Abbreviations:
AUC (Area under the ROC curve), BM (Brain metastases), ctDNA (Circulating tumor DNA), CNS (Central nervous system), DCA (Decision curve analysis), DWI (Diffusion weighted imaging), EGFR (Epidermal growth factor receptor), GLCM (Gray level co-occurrence matrix), GLRLM (Gray level run length matrix), GLSZM (Gray level size zone matrix), GLDM (Gray level dependence matrix), LC (Lung cancer), MRI (Magnetic resonance imaging), MIC (Maximal information coefficient), NSCLC (Non-small cell lung cancer), NGTDM (Neighbouring gray tone difference matrix), RFC (Random forest classifier), ROI (Region of interest), ROC (Receiver operator characteristic curve), TKIs (Tyrosine kinase inhibitors), T1-CE (Contrast-enhanced T1-weighted imaging), T2-FLAIR (T2 fluid-attenuated inversion recovery), TI (Inversion time)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: December 30, 2022
Accepted:
December 16,
2022
Received in revised form:
December 15,
2022
Received:
November 20,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
