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Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography

E, Linning, MD*; Lu, Lin, PhD; Li, Li, PhD; Yang, Hao, MS; Schwartz, Lawrence H., MD; Zhao, Binsheng, PhD

Journal of Computer Assisted Tomography: March/April 2019 - Volume 43 - Issue 2 - p 300–306
doi: 10.1097/RCT.0000000000000836
Cardiovascular and Thoracic Imaging

Objectives The aim of this study was to evaluate the performance of the radiomics method in classifying lung cancer histological subtypes based on multiphasic contrast-enhanced computed tomography (CT) images.

Methods A total of 229 patients with pathologically confirmed lung cancer were retrospectively recruited. All recruited patients underwent nonenhanced and dual-phase chest contrast-enhanced CT; 1160 quantitative radiomics features were calculated to build a radiomics classification model. The performance of the classification models was evaluated by the receiver operating characteristic curve.

Results The areas under the curve of radiomics models in classifying adenocarcinoma and squamous cell carcinoma, adenocarcinoma and small cell lung cancer, and squamous cell carcinoma and small cell lung cancer were 0.801, 0.857, and 0.657 (nonenhanced); 0.834, 0.855, and 0.619 (arterial phase); and 0.864, 0.864, and 0.664 (venous phase), respectively. Moreover, the application of contrast-enhanced CT may affect the selection of radiomics features.

Conclusions Our study indicates that radiomics may be a promising tool for noninvasive predicting histological subtypes of lung cancer based on the multiphasic contrast-enhanced CT images.

From the *Department of Radiology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China;

Department of Radiology, Columbia University Medical Center, New York, NY; and

Department of Pathology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China.

Received for publication September 1, 2018; accepted October 26, 2018.

Correspondence to: Lin Lu, PhD, Department of Radiology, Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (e-mail:

This work was supported by the Natural Science Foundation of Shanxi Province, China (grant 201701D121151).

The authors declare no conflict of interest.

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