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Identifying immunodeficiency status in children with pulmonary tuberculosis: using radiomics approach based on
Hao Ding1,2, Xin Chen1,2, Haoru Wang1,2
1Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China.
Insights
Radiomics analysis of chest CT scans can identify primary immunodeficiency diseases (PIDs) in children with pulmonary tuberculosis (PTB). This approach aids in distinguishing immunocompromised from immunocompetent patients, improving diagnostic accuracy for tuberculosis in vulnerable children.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Children with primary immunodeficiency diseases (PIDs) are at increased risk of severe infections, including Mycobacterium tuberculosis (Mtb).
- Chest computed tomography (CT) is crucial for diagnosing pulmonary tuberculosis (PTB), but distinguishing between immunocompromised and immunocompetent patients can be challenging.
- Radiomics analysis offers a novel approach to extract quantitative features from medical images for diagnostic purposes.
Purpose of the Study:
- To develop and validate a radiomics model using un-enhanced CT scans to identify immunodeficiency status in children diagnosed with PTB.
- To assess the model's efficacy in differentiating children with PIDs from those without PIDs.
Main Methods:
- A retrospective study included 173 pediatric patients with PTB, categorized into PIDs (n=72) and no-PIDs (n=101).
- Radiomics features were extracted from lung lesions on un-enhanced CT images.
- A logistic regression model was built using optimal features identified through dimensionality reduction and validated on training and testing datasets.
Main Results:
- The radiomics model, utilizing nine optimal features, demonstrated strong performance.
- In the training set, the model achieved an AUC of 0.837, sensitivity of 0.783, specificity of 0.780, and F1 score of 0.749.
- The model's performance in the test set included an AUC of 0.746, sensitivity of 0.722, specificity of 0.692, and F1 score of 0.823, with good calibration and clinical utility.
Conclusions:
- CT-based radiomics analysis effectively discriminates between children with and without PIDs among those with PTB.
- This quantitative imaging approach shows significant promise for accurately identifying immunodeficiency status in pediatric PTB cases.
Background:
Children with primary immunodeficiency diseases (PIDs) are particularly vulnerable to infection of Mycobacterium tuberculosis (Mtb). Chest computed tomography (CT) is an important examination diagnosing pulmonary tuberculosis (PTB), and there are some differences between primary immunocompromised and immunocompetent cases with PTB. Therefore, this study aimed to use the radiomics analysis based on un-enhanced CT for identifying immunodeficiency status in children with PTB.
Methods:
This retrospective study enrolled a total of 173 patients with diagnosis of PTB and available immunodeficiency status. Based on their immunodeficiency status, the patients were divided into PIDs (n=72) and no-PIDs (n=101). The samplings were randomly divided into training and testing groups according to a ratio of 3:1. Regions of interest were obtained by segmenting lung lesions on un-enhanced CT images to extract radiomics features. The optimal radiomics features were identified after dimensionality reduction in the training group, and a logistic regression algorithm was used to establish radiomics model. The model was validated in the training and testing groups. Diagnostic efficiency of the model was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, calibration curve, and decision curve.
Results:
The radiomics model was constructed using nine optimal features. In the training set, the model achieved an AUC of 0.837, sensitivity of 0.783, specificity of 0.780, and F1 score of 0.749. The cross-validation of the model in the training set showed an AUC of 0.774, sensitivity of 0.834, specificity of 0.720, and F1 score of 0.749. In the test set, the model achieved an AUC of 0.746, sensitivity of 0.722, specificity of 0.692, and F1 score of 0.823. Calibration curves indicated a strong predictive performance by the model, and decision curve analysis demonstrated its clinical utility.
Conclusions:
The CT-based radiomics model demonstrates good discriminative efficacy in identifying the presence of PIDs in children with PTB, and shows promise in accurately identifying the immunodeficiency status in this population.
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