Journal of thoracic disease · 2025
Whole-lung computed tomography radiomics combined with clinical features for differentiating multidrug-resistant tuberculosis from drug-sensitive tuberculosis: a retrospective multi-center study.
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Song Shulin, Chen Song, Chen Canling, Gan Donghui, Wu Di, Zhu Qindong, Jin Guanqiao, Lu Yibo
Аннотация
BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) poses an escalating public health challenge that complicates diagnosis and treatment. Early detection is crucial for improving the outcomes. This study aimed to evaluate the diagnostic performance of whole-lung computed tomography (CT) radiomics features combined with clinical characteristics in distinguishing MDR-TB from drug-sensitive tuberculosis (DS-TB).
METHODS: This retrospective study included 750 patients with MDR-TB and DS-TB from two hospitals. Clinical data and non-contrast CT images were obtained. The radiomic features were extracted using PyRadiomics. A three-step feature selection process, including t-tests/U-tests, Pearson correlation, and the least absolute shrinkage and selection operator (LASSO), was employed to identify the optimal features. Diagnostic models based on clinical and radiomic features were constructed using LightGBM and multilayer perceptron (MLP) algorithms, respectively. A combined model integrated both types of features. Model performance was assessed using area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score.
RESULTS: Diabetes mellitus and tuberculosis (TB) retreatment were identified as independent risk factors for MDR-TB. The clinical model achieved AUC values of 0.742, 0.738, and 0.725 for training, internal validation, and external validation sets, respectively. Seven radiomics features were selected, with the radiomics model achieving AUC values of 0.724, 0.720, and 0.703. The combined model outperformed the individual models, with AUC values of 0.816, 0.795, and 0.835, and superior sensitivity and specificity.
CONCLUSIONS: Integrating whole-lung CT radiomics with clinical features significantly enhances the diagnostic accuracy of MDR-TB. The combined model outperforms individual models, underscoring the potential of radiomic-clinical data integration. This approach could expand MDR-TB screening coverage without additional economic burden, thereby facilitating prevention and control.
METHODS: This retrospective study included 750 patients with MDR-TB and DS-TB from two hospitals. Clinical data and non-contrast CT images were obtained. The radiomic features were extracted using PyRadiomics. A three-step feature selection process, including t-tests/U-tests, Pearson correlation, and the least absolute shrinkage and selection operator (LASSO), was employed to identify the optimal features. Diagnostic models based on clinical and radiomic features were constructed using LightGBM and multilayer perceptron (MLP) algorithms, respectively. A combined model integrated both types of features. Model performance was assessed using area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score.
RESULTS: Diabetes mellitus and tuberculosis (TB) retreatment were identified as independent risk factors for MDR-TB. The clinical model achieved AUC values of 0.742, 0.738, and 0.725 for training, internal validation, and external validation sets, respectively. Seven radiomics features were selected, with the radiomics model achieving AUC values of 0.724, 0.720, and 0.703. The combined model outperformed the individual models, with AUC values of 0.816, 0.795, and 0.835, and superior sensitivity and specificity.
CONCLUSIONS: Integrating whole-lung CT radiomics with clinical features significantly enhances the diagnostic accuracy of MDR-TB. The combined model outperforms individual models, underscoring the potential of radiomic-clinical data integration. This approach could expand MDR-TB screening coverage without additional economic burden, thereby facilitating prevention and control.