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.
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| http://dx.doi.org/10.12998/wjcc.v8.i21.5203 | DOI Listing |
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