Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Nov 6, 2020; 8(21): 5203-5212
Published online Nov 6, 2020. doi: 10.12998/wjcc.v8.i21.5203
Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans
E-Nuo Cui, Tao Yu, Sheng-Jie Shang, Xiao-Yu Wang, Yi-Lin Jin, Yue Dong, Hai Zhao, Ya-Hong Luo, Xi-Ran Jiang
E-Nuo Cui, Hai Zhao, School of Computer Science and Engineering, Northeastern University, Shenyang 110619, Liaoning Province, China
E-Nuo Cui, School of Computer Science and Engineering, Shenyang University, Shenyang 110044, Liaoning Province, China
Tao Yu, Xiao-Yu Wang, Yue Dong, Ya-Hong Luo, Medical Imaging Department, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, Liaoning Province, China
Sheng-Jie Shang, Yi-Lin Jin, Xi-Ran Jiang, Department of Biomedical Engineering, China Medical University, Shenyang 110122, Liaoning Province, China
Author contributions: Cui EN and Jiang XR conceived and designed the study; Yu T, Zhao H, and Luo YH supported the study; Cui EN, and Wang XY provided the materials or patients; Yu T and Dong Y contributed to the collection and assembly of data; Shang SJ, Jin YL, and Jiang XR contributed to the data analysis and interpretation; and all authors contributed to the manuscript writing and final approval of the manuscript.
Supported by Youth Science and Technology Innovation Leader Support Project, No. RC170497; Shenyang Municipal Science and Technology Project, No. F16-206-9-23; Natural Science Foundation of Liaoning Province of China, No. 201602450; National Key R&D Program of Ministry of Science and Technology of China, No. 2016YFC1303002; National Natural Science Foundation of China, No. 81872363; Major Technology Plan Project of Shenyang, No. 17-230-9-07; Supporting Fund for Big data in Health Care, No. HMB201903101; 2018 Key Research and Guidance Project of Liaoning Province, No. 2018225038.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Liaoning Cancer Hospital and Institute of China Medical University.
Informed consent statement: Patients were not required to give informed consent for the study as the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: The authors declare no conflict of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Xi-Ran Jiang, PhD, Associate Professor, Department of Biomedical Engineering, China Medical University, No. 77 Puhe Road, Shenyang 110122, Liaoning Province, China. xrjiang@cmu.edu.cn
Received: July 21, 2020
Peer-review started: July 21, 2020
First decision: August 8, 2020
Revised: August 12, 2020
Accepted: September 16, 2020
Article in press: September 16, 2020
Published online: November 6, 2020
Processing time: 107 Days and 22.1 Hours
Abstract
BACKGROUND

Pulmonary tuberculosis (TB) and lung cancer (LC) are common diseases with a high incidence and similar symptoms, which may be misdiagnosed by radiologists, thus delaying the best treatment opportunity for patients.

AIM

To develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography (CT) images.

METHODS

We enrolled 478 patients (January 2012 to October 2018), who underwent preoperative CT screening. Radiomics features were extracted and selected from the CT data to establish a logistic regression model. A radiomics nomogram model was constructed, with the receiver operating characteristic, decision and calibration curves plotted to evaluate the discriminative performance.

RESULTS

Radiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance. The radiomics nomogram model exhibited good discrimination, with an area under the curve of 0.914 (sensitivity = 0.890, specificity = 0.796) in the training cohort, and 0.900 (sensitivity = 0.788, specificity = 0.907) in the validation cohort. The decision curve analysis revealed that the constructed nomogram had clinical usefulness.

CONCLUSION

These proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.

Keywords: Pulmonary tuberculosis; Lung cancer; Radiomics; Computed tomography; Computer–aided diagnosis; Nomogram

Core Tip: Pulmonary tuberculosis (TB) often exhibits similarities to lung cancer (LC) on computed tomography (CT) images, which may lead to clinical misdiagnosis. Our study evaluated the discriminative performance of peritumoral regions in differentiating between TB and LC. Radiomics features were extracted and selected from preoperative lung CT images. An eight-feature-combined radiomics signature was constructed as an identifier of TB and LC. A radiomics nomogram model was also plotted and validated with calibration curve and decision curve analyses. The good performance of our model could improve current applications of computer-aided diagnosis for pulmonary TB and LC.