Identifying immunodeficiency status in children with pulmonary tuberculosis: using radiomics approach based on un-enhanced chest computed tomography

Abstract

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.

Keywords: Radiomics; children; differential diagnosis; primary immunodeficiency diseases (PIDs); pulmonary tuberculosis (PTB).

Figure 1

The flowchart of the inclusion and exclusion of study subjects. CT, computed tomography.

Figure 2

A sample of manually delineated ROI of focus in lung. (A) An original image showing focus. (B) The contour of a manual segmenting ROI in the image. (C) The delineated ROI of focus in the lung image. ROI, region of interest.

Figure 3

The ICCs distribution of the radiomics features extracted. ICC, intraclass correlation coefficient.

Figure 4

The curve of ROC of the models on the training cohort (A) and validation cohort (B). ROC, the receiver operating characteristic; AUC, area under the ROC curve.

Figure 5

The developed nomogram of the radiomics models.

Figure 6

DCA and calibration curve of the models. DCA of the training set (A), validation set (B), and test set (C). The cross axis and vertical axis represent the threshold probability value and the net benefit, respectively. Calibration curve for the training (D), validation (E), and test set (F). The calibration curve was used to assess the accuracy of the model. The calibration curve is closer to the diagonal indicated the model is closer to the actual value. DCA, decision curve analysis.