Dataset Information

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree.

ABSTRACT:

Background

Chest computed tomography (CT) scans play an important role in the diagnosis of coronavirus disease 2019 (COVID-19). This study aimed to describe the quantitative CT parameters in COVID-19 patients according to disease severity and build decision trees for predicting respiratory outcomes using the quantitative CT parameters.

Methods

Patients hospitalized for COVID-19 were classified based on the level of disease severity: (1) no pneumonia or hypoxia, (2) pneumonia without hypoxia, (3) hypoxia without respiratory failure, and (4) respiratory failure. High attenuation area (HAA) was defined as the quantified percentage of imaged lung volume with attenuation values between -600 and -250 Hounsfield units (HU). Decision tree models were built with clinical variables and initial laboratory values (model 1) and including quantitative CT parameters in addition to them (model 2).

Results

A total of 387 patients were analyzed. The mean age was 57.8 years, and 50.3% were women. HAA increased as the severity of respiratory outcome increased. HAA showed a moderate correlation with lactate dehydrogenases (LDH) and C-reactive protein (CRP). In the decision tree of model 1, the CRP, fibrinogen, LDH, and gene Ct value were chosen as classifiers whereas LDH, HAA, fibrinogen, vaccination status, and neutrophil (%) were chosen in model 2. For predicting respiratory failure, the decision tree built with quantitative CT parameters showed a greater accuracy than the model without CT parameters.

Conclusions

The decision tree could provide higher accuracy for predicting respiratory failure when quantitative CT parameters were considered in addition to clinical characteristics, PCR Ct value, and blood biomarkers.

SUBMITTER: Kang J

PROVIDER: S-EPMC9163736 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree.

Kang Jieun J Kang Jiyeon J Seo Woo Jung WJ Park So Hee SH Kang Hyung Koo HK Park Hye Kyeong HK Song Je Eun JE Kwak Yee Gyung YG Chang Jeonghyun J Kim Sollip S Kim Ki Hwan KH Park Junseok J Choe Won Joo WJ Lee Sung-Soon SS Koo Hyeon-Kyoung HK

Frontiers in medicine 20220520

<h4>Background</h4>Chest computed tomography (CT) scans play an important role in the diagnosis of coronavirus disease 2019 (COVID-19). This study aimed to describe the quantitative CT parameters in COVID-19 patients according to disease severity and build decision trees for predicting respiratory outcomes using the quantitative CT parameters.<h4>Methods</h4>Patients hospitalized for COVID-19 were classified based on the level of disease severity: (1) no pneumonia or hypoxia, (2) pneumonia witho ...[more]

PMID: 35669915

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Project description:BackgroundWe aimed to develop integrative machine-learning models using quantitative computed tomography (CT) parameters in addition to initial clinical features to predict the respiratory outcomes of coronavirus disease 2019 (COVID-19).MethodsThis was a retrospective study involving 387 patients with COVID-19. Demographic, initial laboratory, and quantitative CT findings were used to develop predictive models of respiratory outcomes. High-attenuation area (HAA) (%) and consolidation (%) were defined as quantified percentages of the area with Hounsfield units between -600 and -250 and between -100 and 0, respectively. Respiratory outcomes were defined as the development of pneumonia, hypoxia, or respiratory failure. Multivariable logistic regression and random forest models were developed for each respiratory outcome. The performance of the logistic regression model was evaluated using the area under the receiver operating characteristic curve (AUC). The accuracy of the developed models was validated by 10-fold cross-validation.ResultsA total of 195 (50.4%), 85 (22.0%), and 19 (4.9%) patients developed pneumonia, hypoxia, and respiratory failure, respectively. The mean patient age was 57.8 years, and 194 (50.1%) were female. In the multivariable analysis, vaccination status and levels of lactate dehydrogenase, C-reactive protein (CRP), and fibrinogen were independent predictors of pneumonia. The presence of hypertension, levels of lactate dehydrogenase and CRP, HAA (%), and consolidation (%) were selected as independent variables to predict hypoxia. For respiratory failure, the presence of diabetes, levels of aspartate aminotransferase, and CRP, and HAA (%) were selected. The AUCs of the prediction models for pneumonia, hypoxia, and respiratory failure were 0.904, 0.890, and 0.969, respectively. Using the feature selection in the random forest model, HAA (%) was ranked as one of the top 10 features predicting pneumonia and hypoxia and was first place for respiratory failure. The accuracies of the cross-validation of the random forest models using the top 10 features for pneumonia, hypoxia, and respiratory failure were 0.872, 0.878, and 0.945, respectively.ConclusionsOur prediction models that incorporated quantitative CT parameters into clinical and laboratory variables showed good performance with high accuracy.

Project description:ObjectivesTo determine whether quantitative parameters derived from dual-energy computed tomography (DECT) were predictive of the aggressiveness of oral tongue squamous cell carcinoma (OTSCC) including the pathologic stages, histologic differentiation, lymph node status, and perineural invasion (PNI).MethodsBetween August 2019 and March 2021, 93 patients (mean age, 54.6 ± 13.8 years; 66 men) with pathologically diagnosed OTSCC were enrolled in this prospective study. Preoperative DECT was performed and quantitative parameters (e.g., slope of the spectral Hounsfield unit curve [λHu], normalized iodine concentration [nIC], normalized effective atomic number [nZeff], and normalized electron density [nRho]) were measured on arterial phase (AP) and venous phase (VP) DECT imaging. Quantitative parameters from DECT were compared between patients with different pathologic stages, histologic differentiation, lymph node statuses, and perineural invasion statuses. Logistic regression analysis was utilized to assess independent parameters and the diagnostic performance was analyzed by the receiver operating characteristic curves (ROC).ResultsλHu and nIC in AP and λHu, nZeff, and nIC in VP were significantly lower in stage III-IV lesions than in stage I-II lesions (p < 0.001 to 0.024). λHu in VP was an independent predictor of tumor stage with an odds ratio (OR) of 0.29, and area under the curve (AUC) of 0.80. λHu and nIC were higher in well-differentiated lesions than in poorly differentiated lesions (p < 0.001 to 0.021). The nIC in VP was an independent predictor of histologic differentiation with OR of 0.31, and AUC of 0.78. λHu and nIC in VP were lower in OTSCCs with lymph node metastasis than those without metastasis (p < 0.001 to 0.005). λHu in VP was the independent predictor of lymph node status with OR of 0.42, and AUC of 0.74. No significant difference was found between OTSCCs without PNI and those with PNI in terms of the quantitative DECT parameters.ConclusionDECT can be a complementary means for the preoperative prediction of the aggressiveness of OTSCC.

Project description:Quantitative parameters from Tc-99m pertechnetate single-photon emission computed tomography/computed tomography (SPECT/CT) are emerging as novel diagnostic markers for functional thyroid diseases. We intended to assess the utility of SPECT/CT parameters in patients with destructive thyroiditis.Thirty-five destructive thyroiditis patients (7 males and 28 females; mean age, 47.3 ± 13.0 years) and 20 euthyroid patients (6 males and 14 females; mean age, 45.0 ± 14.8 years) who underwent Tc-99m pertechnetate quantitative SPECT/CT were retrospectively enrolled. Quantitative parameters from the SPECT/CT (%uptake, standardized uptake value [SUV], thyroid volume, and functional thyroid mass [SUVmean × thyroid volume]) and thyroid hormone levels were investigated to assess correlations and predict the prognosis for destructive thyroiditis. The occurrence of hypothyroidism was the outcome for prognosis.All the SPECT/CT quantitative parameters were significantly lower in the 35 destructive thyroiditis patients compared to the 20 euthyroid patients using the same SPECT/CT scanner and protocol (p < 0.001 for all parameters). T3 and free T4 did not correlate with any SPECT/CT parameters, but thyroid-stimulating hormone (TSH) significantly correlated with %uptake (p = 0.004), SUVmean (p < 0.001), SUVmax (p = 0.002), and functional thyroid mass (p < 0.001). Of the 35 destructive thyroiditis patients, 16 progressed to hypothyroidism. On univariate and multivariate analyses, only T3 levels were associated with the later occurrence of hypothyroidism (p = 0.002, exp(?) = 1.022, 95% confidence interval: 1.008 - 1.035).Novel quantitative SPECT/CT parameters could discriminate patients with destructive thyroiditis from euthyroid patients, suggesting the robustness of the quantitative SPECT/CT approach. However, disease progression of destructive thyroiditis could not be predicted using the parameters, as these only correlated with TSH, but not with T3, the sole predictor of the later occurrence of hypothyroidism.

Project description:BackgroundPeripheral quantitative computed tomography (pQCT) can provide information complementary to dual x-ray absorptiometry (DXA), however, there is sparse normative data to enable meaningful clinical interpretation and comparison. This study aimed to develop age-stratified normative data for pQCT-derived bone parameters in Australian men.MethodsParticipants were men (n = 508, age 33-96 yr) from the Geelong Osteoporosis Study. Bone parameters at 4% (n = 469) and 66% (n = 436) of radial length, and 4% (n = 449) and 66% (n = 438) of tibial length were acquired using pQCT (XCT 2000, Stratec Medizintechnik, Pforzheim, Germany). Best models of age, height and weight for each parameter were developed and where parameters exhibited variation with age, age decade mean (±SD) values were determined. Scatterplots were used to visualise the relationships between each of the parameters and age, height and weight.ResultsThirteen parameters at tibial and radial sites were correlated with age, height and weight, allowing for their inclusion in multiple linear regression models. A positive association with age was found for total area of the tibia or radius (as appropriate) (mm2) at all sites, trabecular bone area (mm2) at 4% sites, and total bone area (both long bones) (mm2) at 66% sites. A negative association with age was found for cortical density (mg/cm3) and cortical thickness (mm) at both radial and tibial 66% sites, but total density (mg/cm3) at the 66% radial site and total cortical density of both long bones (mg/cm3) at the 66% tibial site only.ConclusionThis study presents normative data for pQCT-derived bone parameters and describes age related associations in a number of these variables. Broadly, parameters of bone area were positively associated with age, whereas parameters associated with bone density and structure were negatively associated with age. These data have the potential to be used in clinical settings when assessing age-related decline in bone health.Mini abstractNormative data for pQCT parameters in Australian men are presented, adjusted for age, height and weight.

Dataset Information

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree.

Background

Methods

Results

Conclusions

Publications

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree.

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