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

## TL;DR

This study shows that combining CT radiomics with clinical features improves accuracy in distinguishing focal organizing pneumonia from lung cancer.

## Contribution

The novel contribution is the combined use of CT radiomics and clinical features to enhance diagnostic accuracy for focal organizing pneumonia.

## Key findings

- Pleural adhesion, outer lung zone lesion location, liquefaction necrosis, cavity formation, and long spiculation are key risk factors for FOP.
- The combined model of CT radiomics and clinical features achieved an AUC of 0.955, outperforming either method alone.
- CT radiomics and clinical features both independently contribute to differentiating FOP from peripheral lung cancer.

## Abstract

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).

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.

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).

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.