TY - JOUR
T1 - Predicting EGFR Mutation Status in Non–Small Cell Lung Cancer Using Artificial Intelligence
T2 - A Systematic Review and Meta-Analysis
AU - Nguyen, Hung Song
AU - Ho, Dang Khanh Ngan
AU - Nguyen, Nam Nhat
AU - Tran, Huy Minh
AU - Tam, Ka Wai
AU - Le, Nguyen Quoc Khanh
N1 - Publisher Copyright:
© 2024 The Association of University Radiologists
PY - 2024/2
Y1 - 2024/2
N2 - 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.
AB - 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.
KW - Deep learning
KW - Epidermal growth factor receptor
KW - Machine learning
KW - Non–small cell lung cancer
KW - Radiomics
UR - http://www.scopus.com/inward/record.url?scp=85153877418&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85153877418&partnerID=8YFLogxK
U2 - 10.1016/j.acra.2023.03.040
DO - 10.1016/j.acra.2023.03.040
M3 - Article
C2 - 37120403
AN - SCOPUS:85153877418
SN - 1076-6332
VL - 31
SP - 660
EP - 683
JO - Academic Radiology
JF - Academic Radiology
IS - 2
ER -