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Multicenter Study
. 2024 Sep 19:14:1388991.
doi: 10.3389/fcimb.2024.1388991. eCollection 2024.

A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study

Pulin Li #  1 , Jiling Wang #  2 , Min Tang  1 , Min Li  3 , Rui Han  1 , Sijing Zhou  4 , Xingwang Wu  5 , Ran Wang  1
Affiliations
Multicenter Study

A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study

Pulin Li et al. Front Cell Infect Microbiol. .

Abstract

Purpose: To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from community-acquired pneumonia (CAP).

Methods: A total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis.

Results: Two clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value.

Conclusions: This study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB.

Keywords: community-acquired pneumonia; computed tomography; nomogram; pulmonary tuberculosis; radiomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The radiomics flow chart of the study.
Figure 2
Figure 2
The ROC curves of the three models. (A) Three models of ROC curves in the training cohort. (B) Three models of ROC curves in the testing cohort. (C) Three models of ROC curves in the validation cohort.
Figure 3
Figure 3
The clinical radiomics conjoint nomogram.
Figure 4
Figure 4
The decision curve and precision-recall curve analysis for three models. (A) Three models of decision curve in the training cohort. (B) Three models of decision curve in the testing cohort. (C) Three models of decision curve in the validation cohort. (D) Three models of precision-recall curve in the training cohort. (E) Three models of precision-recall curve in the testing cohort. (F) Three models of precision-recall curve in the validation cohort.
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