Prediction of Pulmonary Ground‐Glass Nodule Progression State on Initial Screening CT Using a Radiomics‐Based Model

BACKGROUND AND OBJECTIVE: Diagnosing pulmonary ground-glass nodules (GGNs) on chest CT imaging remains challenging in clinical practice. Moreover, different stages of GGNs may require different clinical treatments. Hence, we sought to predict the progressive state of pulmonary GGNs (absorption or persistence) for accurate clinical treatment and decision-making. METHODS: We retrospectively enrolled 672 patients (absorption group: 299; control group: 373) from two medical centres from January 2017 to March 2023. Clinical information and radiomic features extracted from regions of interest of all patients on chest CT imaging were collected. All patients were randomly divided into training and test sets at a ratio of 7:3. Three models were constructed-Rad-score (Model 1), clinical factor (Model 2), and clinical factors and Rad-score (Model 3)-to identify GGN progression. In the test dataset, two radiologists (with over 8 years of experience in chest imaging) evaluated the models' performance. Receiver operating characteristic curves, accuracy, sensitivity, and specificity were analysed. RESULTS: In the test set, the area under the curve (AUC) of Model 1 and Model 2 was 0.907 [0.868-0.946] and 0.918 [0.88-0.955], respectively. Model 3 achieved the best predictive performance, with an AUC of 0.959 [0.936-0.982], an accuracy of 0.881, a sensitivity of 0.902, and a specificity of 0.856. The intraclass correlation coefficient of Model 3 (0.86) showed better performance than radiologists (0.83 and 0.71). CONCLUSION: We developed and validated a radiomics-based machine-learning method that achieved good performance in predicting the progressive state of GGNs on initial computed tomography. The model may improve follow-up management of GGNs.