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

Abstract

BACKGROUNDPulmonary 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.AIMTo develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography (CT) images.METHODSWe 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.RESULTSRadiomics 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.CONCLUSIONThese proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.