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Oncologist. 2018 Jul;23(7):806-813. doi: 10.1634/theoncologist.2017-0538. Epub 2018 Apr 5.

Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.

Author information

1
Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
2
Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Korea.
3
School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea.
4
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.
5
Biostatistics and Clinical Epidemiology Center, Samsung Biomedical Research Institute, Seoul, Korea.
6
Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.
7
Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
8
Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea hoyunlee96@gmail.com.

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.

IMPLICATIONS FOR PRACTICE:

Two radiomics features were prognostic for lung cancer survival at multivariate analysis: (a) maximum value of the outer one third of the tumor reflects the tumor microenvironment and (b) size zone variance represents the intratumor heterogeneity. Therefore, a radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and could play a larger role in clinical oncology.

KEYWORDS:

Adenocarcinoma; Computed tomography scans; Lung cancer; Prognosis; Radiomics

Conflict of interest statement

Disclosures of potential conflicts of interest may be found at the end of this article.

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