To explore the clinical significance of computed tomography (CT) radiomics and clinical features in predicting the growth of solitary pulmonary nodules.

A total of 165 patients with solitary pulmonary nodules shown by chest high-resolution computed tomography (HRCT) admitted to our hospital from May 2021 to May 2023 were selected as subjects. They were randomly divided into a training set (115 cases) and a validation set (50 cases) at a ratio of 7: 3. Radiomics feature variables based on CT images were extracted using the open-source Python package Pyradiomics. The growth of pulmonary nodules was observed during a 2-year follow-up. The maximum relevance minimum redundancy method and LASSO analysis were used to screen imaging feature variables, and a model was constructed. The receiver operating characteristic (ROC) curve was used to compare the predictive value of the model.

Among 165 cases, there were 99 growing pulmonary nodules (60.00%) and 66 stable pulmonary nodules (40.00%). LASSO analysis identified 5 features (original_shape_MajorAxisLength, log.sigma.1.0.mm.3D_glrlm_ShortRunLowGrayLevelEmphasis, log.sigma.5.0.mm.3D_firstorder_90Percentile, wavelet.LHH_gldm_LargeDependenceHighGrayLevelEmphasis, wavelet.LLL_glcm_JointEntropy) as the radiomics signature for predicting pulmonary nodule growth, and a radiomics score was established (Rad-score=0.4535973-0.3413027×original_shape_MajorAxisLength-0.113593×log.sigma.1.0.mm.3D_glrlm_ShortRunLowGrayLevelEmphasis+ 0.5150988×log.sigma.5.0.mm.3D_firstorder_90Percentile-0.7143545×wavelet.LHH_gldm_LargeDependenceHighGrayLevelEmphasis+ 0.10314×wavelet.LLL_glcm_JointEntropy). Multivariate logistic regression analysis showed that older age (OR=1.066, 95%CI: 1.018~1.117), female gender (OR=0.313, 95%CI: 0.113~0.870), solid nodule type (OR=0.495, 95%CI: 0.279~0.878), and high Rad-score (OR=1.038, 95%CI: 1.020~1.057) were risk factors for pulmonary nodule growth. The combined model (age, gender, nodule type+ Rad-score) had good discriminative ability in both the training set and the validation set area under the curve(AUC) of ROC 0.871 (95%CI: 0.804~0.939) vs. 0.873 (95%CI: 0.776~0.971). The Hosmer-Lemeshow test results for the training set and validation set were P=0.664 and P=0.502, respectively, indicating no statistically significant difference between the predicted probability and the actual probability of the combined model. Decision curve analysis showed that the combined model had clinical application value within the high-risk threshold range training set 0.60~0.95, validation set 0.60~0.93.

The nomogram model combining chest HRCT-derived radiomics features with age, gender, and nodule type has the potential to accurately predict the short-term growth risk of pulmonary nodules. This model has shown certain clinical value in helping clinical doctors develop diagnosis and treatment strategies.