Diagnostic value of CT radiomics and clinical features in differentiating focal organizing pneumonia from peripheral lung cancer

Abstract

Objective: This study aimed to evaluate the diagnostic value of computed tomography (CT) radiomics combined with clinical characteristics in differentiating focal organizing pneumonia (FOP) from peripheral lung cancer (PLC).

Methods: A total of 60 FOP patients admitted between June 2023 and June 2024 were included as the FOP group, while 60 PLC patients were assigned to the PLC group. General clinical and imaging data were collected for both groups. Logistic regression analysis was employed to identify independent risk factors for FOP. Radiomics features were extracted from CT images of FOP patients, and the Lasso method was used to select key radiomics features and calculate CT radiomics scores. The diagnostic performance of CT radiomics and clinical characteristics for FOP was assessed using receiver operating characteristic (ROC) curve analysis.

Results: There were no statistically significant differences in age, gender, lung tissue boundary, pleural indentation sign, vascular convergence sign, pleural traction sign, or bronchial air sign between the FOP and PLC groups (P > 0.05). However, significant differences were observed in pleural adhesion, lesion location in the outer lung zone, liquefaction necrosis, cavity formation, and spiculation (P < 0.05). Logistic regression analysis identified pleural adhesion, lesion location in the outer lung zone, liquefaction necrosis, cavity formation, and long spiculation as independent risk factors for FOP (P < 0.05). ROC curve analysis demonstrated that the area under the curve (AUC) for clinical characteristics and CT radiomics in diagnosing FOP were 0.895 and 0.859, respectively. Notably, the AUC for the combined model integrating CT radiomics and clinical characteristics was 0.955, which was significantly higher than that of either approach alone (P < 0.05).

Conclusion: Pleural adhesion, lesion location in the outer lung zone, liquefaction necrosis, cavity formation, and long spiculation are key risk factors for FOP. Both CT radiomics and clinical characteristics can aid in the differentiation of FOP from PLC, and their combination significantly enhances diagnostic accuracy.

Keywords: clinical features; computed tomography; focal organizing pneumonia; peripheral lung cancer; radiomics.

Figure 1

Distinct imaging features of FOP and PLC.

Figure 2

Radiomics feature selection using LASSO regression. (A) Coefficient profiles of variables under different values of log λ. (B) Selection of the optimal λ based on binomial deviance.

Figure 3

ROC curves for the diagnosis of FOP using CT radiomics and clinical features. Receiver operating characteristic (ROC) curves illustrating the diagnostic performance of Clinical Features (CF), CT Radiomics Features (CRF), and the Combined Radiomics and Clinical Features (CRCF) model in distinguishing FOP from PLC.