0 15

Cited 0 times in

Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer

Authors
 Yae Won Park  ;  Chansik An  ;  JaeSeong Lee  ;  Kyunghwa Han  ;  Dongmin Choi  ;  Sung Soo Ahn  ;  Hwiyoung Kim  ;  Sung Jun Ahn  ;  Jong Hee Chang  ;  Se Hoon Kim  ;  Seung-Koo Lee 
Citation
 NEURORADIOLOGY, Vol.63(3) : 343-352, 2021-03 
Journal Title
 NEURORADIOLOGY 
ISSN
 0028-3940 
Issue Date
2021-03
Keywords
Diffusion tensor ; Epidermal growth factor receptor ; Imaging ; Machine learning ; Magnetic resonance imaging ; Radiomics
Abstract
Purpose: To assess whether the radiomic features of diffusion tensor imaging (DTI) and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) mutation status in brain metastases from non-small cell lung cancer (NSCLC). Methods: A total of 99 brain metastases in 51 patients who underwent surgery or biopsy with underlying NSCLC and known EGFR mutation statuses (57 from EGFR wild type, 42 from EGFR mutant) were allocated to the training (57 lesions in 31 patients) and test (42 lesions in 20 patients) sets. Radiomic features (n = 526) were extracted from preoperative MR images including T1C and DTI. Radiomics classifiers were constructed by combinations of five feature selectors and four machine learning algorithms. The trained classifiers were validated on the test set, and the classifier performance was assessed by determining the area under the curve (AUC). Results: EGFR mutation status showed an overall discordance rate of 12% between the primary tumors and corresponding brain metastases. The best performing classifier was a combination of the tree-based feature selection and linear discriminant algorithm and 5 features were selected (1 from ADC, 2 from fractional anisotropy, and 2 from T1C images), resulting in an AUC, accuracy, sensitivity, and specificity of 0.73, 78.6%, 81.3%, and 76.9% in the test set, respectively. Conclusions: Radiomics classifiers integrating multiparametric MRI parameters may have potential in differentiating the EGFR mutation status in brain metastases from NSCLC.
Full Text
https://link.springer.com/article/10.1007%2Fs00234-020-02529-2
DOI
10.1007/s00234-020-02529-2
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Se Hoon(김세훈) ORCID logo https://orcid.org/0000-0001-7516-7372
Kim, Hwiyoung(김휘영)
Park, Yae Won(박예원) ORCID logo https://orcid.org/0000-0001-8907-5401
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Ahn, Sung Jun(안성준) ORCID logo https://orcid.org/0000-0003-0075-2432
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
Chang, Jong Hee(장종희) ORCID logo https://orcid.org/0000-0003-1509-9800
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/182405
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links