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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Differentiation of nodular pulmonary infections in immunocompromised patients using CT-based radiomics
Yanni Mahiou1, Loïc Duron2, Antoine Decoux1
1Université Paris Cité, PARCC UMRS 970, INSERM, Paris 75015, France.
Insights
A computed tomography (CT)-based radiomic model shows promise in distinguishing tuberculosis from other lung infections in immunocompromised patients. This quantitative analysis approach may aid in diagnosing pulmonary infections.
Area of Science:
- Radiology
- Medical Imaging
- Infectious Diseases
Background:
- Immunocompromised patients are susceptible to severe pulmonary infections.
- Accurate differentiation of infectious agents is crucial for effective treatment.
- Computed tomography (CT) is a primary imaging modality for diagnosing pulmonary infections.
Purpose of the Study:
- To develop and evaluate a CT-based radiomic model for discriminating infectious agents in immunocompromised patients with pulmonary infections.
- To compare the performance of radiomic, clinical/semantic, and combined models in this diagnostic task.
Main Methods:
- Retrospective analysis of CT scans from 196 immunocompromised patients with pulmonary infections (aspergillosis, tuberculosis, mucormycosis/nocardiosis).
- Extraction of radiomic features from lung lesions and collection of clinical/semantic features.
- Development of multi-class random forest models (radiomic, clinical/semantic, combined) using 5-fold cross-validation.
- Performance evaluation using the area under the receiver operating characteristic curve (AUC).
Main Results:
- The radiomic model achieved a mean AUC of 0.84, outperforming the clinical/semantic model (AUC 0.66).
- The combined model showed similar performance to the radiomic model (AUC 0.82).
- Radiomic and combined models demonstrated significantly higher AUCs for differentiating tuberculosis compared to the clinical/semantic model.
Conclusions:
- The CT-based radiomic model demonstrates superior performance in differentiating tuberculosis from other nodular lung lesions in immunocompromised patients.
- Quantitative analysis using radiomics holds potential for improving the diagnosis of pulmonary infections in this vulnerable population.
- Further validation of radiomic models is warranted for clinical application in infectious disease diagnosis.
Purpose:
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.
Materials And Methods:
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).
Results:
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).
Conclusions:
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.
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