Rationale and Objectives
Recent advancements in artificial intelligence (AI) render a substantial promise for
epidermal growth factor receptor (EGFR) mutation status prediction in non–small cell
lung cancer (NSCLC). We aimed to evaluate the performance and quality of AI algorithms
that use radiomics features in predicting EGFR mutation status in patient with NSCLC.
Materials and Methods
We searched PubMed (Medline), EMBASE, Web of Science, and IEEExplore for studies published
up to February 28, 2022. Studies utilizing an AI algorithm (either conventional machine
learning [cML] and deep learning [DL]) for predicting EGFR mutations in patients with
NSLCL were included. We extracted binary diagnostic accuracy data and constructed
a bivariate random-effects model to obtain pooled sensitivity, specificity, and 95%
confidence interval. This study is registered with PROSPERO, CRD42021278738.
Results
Our search identified 460 studies, of which 42 were included. Thirty-five studies
were included in the meta-analysis. The AI algorithms exhibited an overall area under
the curve (AUC) value of 0.789 and pooled sensitivity and specificity levels of 72.2%
and 73.3%, respectively. The DL algorithms outperformed cML in terms of AUC (0.822
vs. 0.775) and sensitivity (80.1% vs. 71.1%), but had lower specificity (70.0% vs.
73.8%, p-value < 0.001) compared to cML. Subgroup analysis revealed that the use of
positron-emission tomography/computed tomography, additional clinical information,
deep feature extraction, and manual segmentation can improve diagnostic performance.
Conclusion
DL algorithms can serve as a novel method for increasing predictive accuracy and thus
have considerable potential for use in predicting EGFR mutation status in patient
with NSCLC. We also suggest that guidelines on using AI algorithms in medical image
analysis should be developed with a focus on oncologic radiomics.
Key Words
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Article info
Publication history
Published online: April 28, 2023
Accepted:
March 28,
2023
Received in revised form:
March 25,
2023
Received:
February 23,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
