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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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Project description:PurposeComputed tomography (CT)-derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT-Perfusion (CT-P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion.MethodsCT-Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT-P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT-P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT-ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ . Spatial Spearman correlation between single photon emission CT perfusion (SPECT-P) and the proposed CT-P method was assessed in two patient cohorts via a parameter sweep of τ . The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT-P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT-P and 4DCT images acquired prior to radiotherapy. For each test case, CT-P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT-P and the resulting CT-P images were computed.ResultsThe median correlations between CT-P and SPECT-P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT-P and SPECT-P correlations across all 30 test cases ranged between 0.02 and 0.82. A one-sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two-sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One-sample sign test was statistically significant with 96.5 % confidence interval: 0.20-0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one-sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45-0.71, P < 0.00001.ConclusionCT-Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT-P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT-P imaging.

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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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