Stratified differentiation of 3 common pulmonary nodules with CT radiomics

Abstract

Objective To explore the value of a logistic regression (LR) model based on CT radiomics features for the stratified classification of isolated adenocarcinoma pulmonary nodules, tuberculous nodules and non-tuberculous inflammatory pulmonary nodules. Methods The clinical and CT data of the patients pathologically diagnosed with pulmonary adenocarcinoma, tuberculosis, and non-tuberculous inflammatory nodules in our hospital between January 2018 and January 2022 were collected and retrospectively analyzed. By contouring the region of interest for pulmonary nodules and extracting CT radiomics features, prediction models were established to distinguish pulmonary adenocarcinoma vs inflammatory nodules and tuberculosis vs non-tuberculous inflammatory nodules. The model's performance was assessed by plotting receiver operating characteristic (ROC) curves and calculating area under curve (AUC), sensitivity, and specificity. Results A total of 526 solitary pulmonary nodules were collected, including 263 cases of pulmonary adenocarcinoma, 99 cases of tuberculous nodules, and 164 cases of non-tuberculous inflammatory nodules. In the training and validation sets, the LR models based on CT radiomics features and clinical risk factors achieved an AUC value of 0.880 and 0.886, respectively, for distinguishing pulmonary adenocarcinoma from inflammatory nodules. For discriminating tuberculosis versus non-tuberculous inflammatory nodules, the LR models based on CT radiomics features yielded an AUC value of 0.921 in the training set and of 0.853 in the validation set. Conclusion The LR prediction models based on CT radiomics features demonstrate excellent performance in hierarchically identifying the 3 prevalent solitary pulmonary nodules, with substantial clinical significance.

Keywords

• pulmonary nodule
• computed tomography
• radiomics
• nomogram
• diagnosis