Differentiation of nodular pulmonary infections in immunocompromised patients using CT-based radiomics

The purpose of this study was to develop a computed tomography (CT)-based radiomic model and evaluate its performance in discriminating between different infectious agents in immunocompromised patients with pulmonary infection.

This single-center retrospective study included immunocompromised patients with pulmonary infections presenting as focal, nodular lung lesion(s) on CT from 2012 to 2023. Thirteen clinical and CT semantic features were collected. Three-dimensional segmentation of the main lung lesion was performed on CT images, followed by radiomic feature extraction. The dataset was divided into training (80 %) and test (20 %) sets. Radiomic, clinical/semantic, and combined models were built using a multi-class random forest classifier and a 5-fold stratified cross-validation in the training set and tested in the test set. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC).

One hundred and ninety-six patients with aspergillosis (n = 123), tuberculosis (n = 41), or "mucormycosis/nocardiosis" (n = 32) were included. There were 131 men and 65 women with a median age of 67 years (age range: 20-95 years). The mean AUC of the radiomic model was 0.84 (95 % confidence interval [CI]: 0.66, 0.98), whereas the mean AUC of the clinical/semantic model was 0.66 [95 % CI: 0.46, 0.84]). The mean AUC of the combined model was similar to that of the radiomic model (0.82 [95 % CI: 0.63, 0.97]). The AUCs of the radiomic model (0.89 [95 % CI: 0.74, 1.00]) and of the combined models (0.93 [95 % CI: 0.83, 0.99]) for discriminating tuberculosis from the other two classes were significantly higher than the AUC of the clinical/semantic model (0.62 [95 % CI: 0.41, 0.81]) (P = 0.046 and 0.003, respectively).

In our data set, the radiomic model performs better than a clinical/semantic model in differentiating tuberculosis from other nodular lung lesions. These encouraging results highlight the potential role of quantitative analysis in contributing to the diagnosis of pulmonary infections in immunocompromised patients.