Predictive radiogenomics modeling of EGFR mutation status in lung cancer

Gevaert, Olivier; Echegaray, Sebastian; Khuong, Amanda; Hoang, Chuong D.; Shrager, Joseph B.; Jensen, Kirstin C.; Berry, Gerald J.; Guo, H. Henry; Lau, Charles; Plevritis, Sylvia K.; Rubin, Daniel L.; Napel, Sandy; Leung, Ann N.

doi:10.1038/srep41674

Cited by 161Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and TreatmentLung Cancer Treatments and Mutations

Predictive radiogenomics modeling of EGFR mutation status in lung cancer

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Gevaert, Olivier, Echegaray, Sebastian, Khuong, Amanda, Hoang, Chuong D., Shrager, Joseph B., Jensen, Kirstin C., Berry, Gerald J., Guo, H. Henry +5 more·2017·Scientific Reports

DOI 10.1038/srep41674PMC PMC5282551

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Abstract

Molecular analysis of the mutation status for EGFR and KRAS are now routine in the management of non-small cell lung cancer. Radiogenomics, the linking of medical images with the genomic properties of human tumors, provides exciting opportunities for non-invasive diagnostics and prognostics. We investigated whether EGFR and KRAS mutation status can be predicted using imaging data. To accomplish this, we studied 186 cases of NSCLC with preoperative thin-slice CT scans. A thoracic radiologist annotated 89 semantic image features of each patient's tumor. Next, we built a decision tree to predict the presence of EGFR and KRAS mutations. We found a statistically significant model for predicting EGFR but not for KRAS mutations. The test set area under the ROC curve for predicting EGFR mutation status was 0.89. The final decision tree used four variables: emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin. The presence of either of the first two features predicts a wild type status for EGFR while the presence of any ground glass component indicates EGFR mutations. These results show the potential of quantitative imaging to predict molecular properties in a non-invasive manner, as CT imaging is more readily available than biopsies.

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Many studies have shown that imaging features are significantly associated with patterns of gene expression and genetic mutations.
“Many studies have shown that imaging features are significantly associated with patterns of gene expression and genetic mutations, demonstrating that radiogenomic analysis can identify different biological mechanisms by means of mathematical and computational devices, enabling the decoding of disease phenotypes by noninvasive methods(48,49).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [48]

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Authors: Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Jensen, K. C., Berry, G. J., Guo, H. H., Lau, C., Plevritis, S. K., Rubin, D. L., Napel, S., & Leung, A. N.
Affiliations: Stanford Medicine (US)
National Institutes of Health (US)
VA Palo Alto Health Care System (US)
Corresponding author: Gevaert, Olivier
ORCID: Gevaert, Olivier
Echegaray, Sebastian
Hoang, Chuong D.
Shrager, Joseph B.
Berry, Gerald J.
Guo, H. Henry
Lau, Charles
Plevritis, Sylvia K.
Rubin, Daniel L.
Napel, Sandy
Leung, Ann N.
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2017-01-31
Volume: 7
Issue: 1
Pages: 41674-41674
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/srep41674
PMC: PMC5282551
OpenAlex: W2584323939
Cited by: 160
References: 37
Citations by year: 2026: 9 · 2025: 11 · 2024: 8 · 2023: 20 · 2022: 26 · 2021: 25 · 2020: 25 · 2019: 16 · 2018: 16 · 2017: 4
FWCI: 12.80
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Scientific Reports (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: National Institutes of Health
National Cancer Institute (U01CA190214, R01CA160251, U01CA187947)
National Institute of Biomedical Imaging and Bioengineering (R01EB020527)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
Keywords: radiogenomics, EGFR mutation, KRAS mutation, non-small cell lung cancer, computed tomography, tumor margin, emphysema, decision tree
MeSH terms: Decision Trees, Female, Humans, Lung Neoplasms, Male, Mutation, Biomarkers, Tumor, Multivariate Analysis, Proto-Oncogene Proteins p21(ras), Area Under Curve, Genomics, ErbB Receptors

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Predictive radiogenomics modeling of EGFR mutation status in lung cancer

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