Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans

World J Clin Cases. 2020 Nov 6;8(21):5203-5212. doi: 10.12998/wjcc.v8.i21.5203.

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