Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication

Cited 0 time in webofscience Cited 0 time in scopus
10 Viewed 0 Downloaded
Title
Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication
Author(s)
Geewon Lee; Hyunjun Park; Insuk Sohn; Seung-Hak Lee; So Hee Song; Hyeseung Kim; Kyung Soo Lee; Young Mog Shim; Ho Yun Lee
Publication Date
2018-07
Journal
ONCOLOGIST, v.23, no.7, pp.806 - 813
Publisher
WILEY-BLACKWELL
Abstract
Background. In this era of personalized medicine, there is an expanded demand for advanced imaging biomarkers that reflect the biology of the whole tumor. Therefore, we investigated a large number of computed tomography-derived radiomics features along with demographics and pathology-related variables in patients with lung adenocarcinoma, correlating them with overall survival. Materials and Methods. Three hundred thirty-nine patients who underwent operation for lung adenocarcinoma were included. Analysis was performed using 161 radiomics features, demographic, and pathologic variables and correlated each with patient survival. Prognostic performance for survival was compared among three models: (a) using only clinicopathological data; (b) using only selected radiomics features; and (c) using both clinicopathological data and selected radiomics features. Results. At multivariate analysis, age, pN, tumor size, type of operation, histologic grade, maximum value of the outer 1/3 of the tumor, and size zone variance were statistically significant variables. In particular, maximum value of outer 1/3 of the tumor reflected tumor microenvironment, and size zone variance represented intratumor heterogeneity. Integration of 31 selected radiomics features with clinicopathological variables led to better discrimination performance. Conclusion. Radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and has potential to improve prognosis assessment in clinical oncology (c) AlphaMed Press 2018
URI
https://pr.ibs.re.kr/handle/8788114/5251
ISSN
1083-7159
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > Journal Papers (저널논문)
Files in This Item:
10_박현진_Comprehensive computed tomography radiomics analysis of lung adenocarcinoma for prognostication.pdfDownload

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

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

Browse