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

Abstract

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 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 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 lesion showed the best discriminative performance. The radiomics nomogram model exhibited good discrimination, with an area under the curve of 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 revealed that the constructed nomogram had clinical usefulness. These proposed radiomic methods can be used as a noninvasive tool for based on preoperative CT data.