Tuberculosis masked by immunodeficiency: a review of two cases diagnosed with chronic granulomatous disease

Nacaroğlu, Hikmet Tekin; Erdem, Semiha Bahçeci; Gülez, Nesrin; Karkıner, Canan Şule Ünsal; Devrim, İlker; Genel, Ferah; Köker, Mustafa Yavuz; Can, Demet

doi:10.5578/tt.22093

Cited by 1 publication

(1 citation statement)

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“…Chest computed tomography (CT) plays an irreplaceable role in lung disease, and the pulmonary manifestations can be used to evaluate the patient’s condition for PIDs and PTB ( 11 ). Some studies have shown that there are differences in PTB between immunocompromised and immunocompetent patients, such as the distribution and pattern of lesions ( 12 - 14 ).…”

Section: Introductionmentioning

confidence: 99%

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

Ding,

Chen,

Wang

et al. 2023

Transl Pediatr

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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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Section: Introductionmentioning

confidence: 99%