Rationale and Objectives
We aim to explore the value of chest CT radiomics in predicting the epidermal growth
factor receptor (EGFR)-T790M resistance mutation of advanced non-small cell lung cancer
(NSCLC) patients after the failure of first-line EGFR-tyrosine kinase inhibitor (EGFR-TKI).
Materials and Methods
A total of 211 and 135 advanced NSCLC patients with tumor tissue-based (Cohort-1)
or circulating tumor DNA (ctDNA)-based (Cohort-2) EGFR-T790M testing were included,
respectively. Cohort-1 was used for modeling and Cohort-2 was for models’ validation.
Radiomic features were extracted from tumor lesions on chest nonenhanced CT (NECT)
and/or contrast-enhanced CT (CECT). We used eight feature selectors and eight classifier
algorithms to establish radiomic models. Models were evaluated by area under the receiver
operating characteristic curve (AUC), calibration curve, and decision curve analysis
(DCA).
Results
CT morphological manifestations of peripheral location and pleural indentation sign
were associated with EGFR-T790M. For NECT, CECT, and NECT+CECT radiomic features,
the feature selector and classifier algorithms of LASSO and Stepwise logistic regression,
Boruta and SVM, and LASSO and SVM were chosen to develop the optimal model, respectively
(AUC: 0.844, 0.811, and 0.897). All models performed well in calibration curves and
DCA. Independent validation of models in Cohort-2 revealed that both NECT and CECT
models individually had limited power for predicting EGFR-T790M mutation detected
by ctDNA (AUC: 0.649, 0.675), while the NECT+CECT radiomic model had a satisfactory
AUC (0.760).
Conclusion
This study proved the feasibility of using CT radiomic features to predict the EGFR-T790M
resistance mutation, which could be helpful in guiding personalized therapeutic strategies.
Key Words
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Article info
Publication history
Published online: March 18, 2023
Accepted:
January 31,
2023
Received in revised form:
January 25,
2023
Received:
December 28,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
