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Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children.

ABSTRACT: BACKGROUND:To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children. METHODS:This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n?=?86, validation cohort n?=?29). A total of 970 radiomic features were extracted from CT images and key features were screened out to build radiomic signatures using the least absolute shrinkage and selection operator algorithm. A predictive nomogram was developed based on the signatures and clinical factors, and its performance was assessed by the receiver operating characteristic curve, calibration curve, and decision curve analysis. RESULTS:Initially, 5 and 6 key features were selected to establish a radiomic signature from the pulmonary consolidation region (RS1) and a signature from lymph node region (RS2), respectively. A predictive nomogram was built combining RS1, RS2, and a clinical factor (duration of fever). Its classification performance (AUC?=?0.971, 95% confidence interval [CI]: 0.912-1) was better than the senior radiologist's clinical judgment (AUC?=?0.791, 95% CI: 0.636-0.946), the clinical factor (AUC?=?0.832, 95% CI: 0.677-0.987), and the combination of RS1 and RS2 (AUC?=?0.957, 95% CI: 0.889-1). The calibration curves indicated a good consistency of the nomogram. Decision curve analysis demonstrated that the nomogram was useful in clinical settings. CONCLUSIONS:A CT-based predictive nomogram was proposed and could be conveniently used to differentiate pulmonary TB from CAP in children.

SUBMITTER: Wang B

PROVIDER: S-EPMC6688341 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children.

Wang Bei B Li Min M Ma He H Han Fangfang F Wang Yan Y Zhao Shunying S Liu Zhimin Z Yu Tong T Tian Jie J Dong Di D Peng Yun Y

BMC medical imaging 20190808 1

<h4>Background</h4>To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children.<h4>Methods</h4>This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n = 86, validation cohort n = 29). A total of 970 radiomic fea ...[more]

PMID: 31395012

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Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children.

Publications

Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children.

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