Computed Tomography-Based Radiomics Analysis of Different Machine Learning Approaches for Differentiating Pulmonary Sarcomatoid Carcinoma and Pulmonary Inflammatory Pseudotumor

Abstract

Background: Differentiating between pulmonary sarcomatoid carcinoma (PSC) and pulmonary inflammatory pseudotumor (PIP) is challenging using current conventional diagnostic methods. This lack of distinction significantly impacts subsequent clinical treatment decisions. Objectives: This study was conducted to construct an effective method to distinguish between PSC and PIP based on commonly used computed tomography (CT) images. Patients and Methods: A total of 14 patients with PSC and 76 patients with PIP were retrospectively included in the study for CT imaging. Radiomics features were extracted from non-enhanced CT images, and canonical correlation analysis was performed to reduce redundancy. The final radiomics signature was then identified using the least absolute shrinkage and selection operator (LASSO). Logistic regression (LR), classification and regression trees (CART), support vector machine (SVM), k-nearest neighbors (KNN), and gradient boosting machine (GBM) were used to construct the radiomics models. The performance of these different radiomics models was evaluated using the receiver operating characteristic curve. Results: A total of 1186 radiomics features were extracted from non-enhanced CT images. After dimensionality reduction and selection, 7 valuable features were identified. The performance of 5 machine learning models was evaluated to differentiate between PSC and PIP, and the GBM-based radiomics model demonstrated the best performance. The GBM-based radiomics model achieved an accuracy of 0.922, area under the curve (AUC) of 0.98, F1 score of 0.967, and log loss of 0.161. Compared to conventional clinical-radiological diagnosis, the GBM-based radiomics model showed a significant association (odds ratio [OR] = 8.119; P = 0.006). Conclusion: The implementation of the GBM-based radiomics model has the potential to improve the ability to differentiate between PSC and PIP, thereby influencing the timeliness of subsequent surgical interventions and even the prognosis of patients.