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Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer

Authors
 Sung Jun Ahn  ;  Hyeokjin Kwon  ;  Jin-Ju Yang  ;  Mina Park  ;  Yoon Jin Cha  ;  Sang Hyun Suh  ;  Jong-Min Lee 
Citation
 SCIENTIFIC REPORTS, Vol.10(1) : 8905, 2020-06 
Journal Title
 SCIENTIFIC REPORTS 
Issue Date
2020-06
Abstract
Identification of EGFR mutations is critical to the treatment of primary lung cancer and brain metastases (BMs). Here, we explored whether radiomic features of contrast-enhanced T1-weighted images (T1WIs) of BMs predict EGFR mutation status in primary lung cancer cases. In total, 1209 features were extracted from the contrast-enhanced T1WIs of 61 patients with 210 measurable BMs. Feature selection and classification were optimized using several machine learning algorithms. Ten-fold cross-validation was applied to the T1WI BM dataset (189 BMs for training and 21 BMs for the test set). Area under receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity were calculated. Subgroup analyses were also performed according to metastasis size. For all measurable BMs, random forest (RF) classification with RF selection demonstrated the highest diagnostic performance for identifying EGFR mutation (AUC: 86.81). Support vector machine and AdaBoost were comparable to RF classification. Subgroup analyses revealed that small BMs had the highest AUC (89.09). The diagnostic performance for large BMs was lower than that for small BMs (the highest AUC: 78.22). Contrast-enhanced T1-weighted image radiomics of brain metastases predicted the EGFR mutation status of lung cancer BMs with good diagnostic performance. However, further study is necessary to apply this algorithm more widely and to larger BMs.
Files in This Item:
T202002700.pdf Download
DOI
10.1038/s41598-020-65470-7
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Park, Mina(박미나) ORCID logo https://orcid.org/0000-0002-2005-7560
Suh, Sang Hyun(서상현) ORCID logo https://orcid.org/0000-0002-7098-4901
Ahn, Sung Jun(안성준) ORCID logo https://orcid.org/0000-0003-0075-2432
Cha, Yoon Jin(차윤진) ORCID logo https://orcid.org/0000-0002-5967-4064
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179299
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