Rationale and Objectives
The research aims to investigate whether MRI radiomics on hepatic metastasis from
primary nonsmall cell lung cancer (NSCLC) can be used to differentiate patients with
epidermal growth factor receptor (EGFR) mutations from those with EGFR wild-type,
and develop a prediction model based on combination of primary tumor and the metastasis.
Materials and Methods
A total of 130 patients were enrolled between Aug. 2017 and Dec. 2021, all pathologically
confirmed harboring hepatic metastasis from primary NSCLC. The pyradiomics was used
to extract radiomics features from intra- and peritumoral areas of both primary tumor
and metastasis. The least absolute shrinkage and selection operator (LASSO) regression
was applied to identify most predictive features and to develop radiomics signatures
(RSs) for prediction of the EGFR mutation status. The receiver operating characteristic
(ROC) curve analysis was performed to assess the prediction capability of the developed
RSs.
Results
A RS-Primary and a RS-Metastasis were derived from the primary tumor and metastasis,
respectively. The RS-Combine by combination of the primary tumor and metastasis achieved
the highest prediction performance in the training (AUCs, RS-Primary vs. RS-Metastasis
vs. RS-Combine, 0.826 vs. 0.821 vs. 0.908) and testing (AUCs, RS-Primary vs. RS-Metastasis
vs. RS-Combine, 0.760 vs. 0.791 vs. 0.884) set. The smoking status showed significant
difference between EGFR mutant and wild-type groups (p < 0.05) in the training set.
Conclusion
The study indicates that hepatic metastasis-based radiomics can be used to detect
the EGFR mutation. The developed multiorgan combined radiomics signature may be helpful
to guide individual treatment strategies for patients with metastatic NSCLC.
Key Words
Abbreviations:
CET1 (contrast-enhanced T1-weighted), EGFR (epidermal growth factor receptor), Gd-DTPA (gadolinium-diethylenetriamine penta-acetic acid), GLCM (gray level co-occurrence matrix), GLDM (gray level dependence matrix), GLRLM (gray level run length matrix), GLSZM (gray level size zone matrix), ICC (intraclass correlation coefficient), LA (lung adenocarcinoma), LASSO (least absolute shrinkage and selection operator), MRI (magnetic resonance imaging), NGTDM (neighboring gray tone difference matrix), NSCLC (nonsmall cell lung cancer), ROC (receiver operating characteristic), ROI (region of interest), RSs (radiomics signatures), TKIs (tyrosine kinase inhibitors)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: July 27, 2022
Accepted:
June 23,
2022
Received in revised form:
June 20,
2022
Received:
May 11,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
