Chest Computed Tomography Parameters Research Articles

Appropriateness of recommended chest compression depths for cardiopulmonary resuscitation based on chest computed tomography parameters among Thai population: A multicenter retrospective study

AimWe evaluated the appropriateness of various chest compression (CC) depths among Thai population by comparing the calculated heart compression fraction (HCF) using mathematical methods based on chest computed tomography (CT) measurements. MethodsThis multicenter retrospective cross-sectional study was conducted from September 2014 to December 2020. Chest parameters included external anteroposterior diameter (EAPD), internal anteroposterior diameter (IAPD), heart anteroposterior diameter (HAPD), and non-cardiac soft tissue measured at the level of maximum left ventricular diameter (LVmax). We compared the HCFs as calculated from CT parameters using different CC depths at 5 cm, 6 cm, 1/4 of EAPD, and 1/3 of EAPD, with further subgroup analysis stratified by sex and BMI. ResultsA total of 2927 eligible adult patients with contrast-enhanced chest CT were included. The study group had mean age of 60.1 ± 14.7 years, mean BMI of 22 ± 4.4 kg/m2, and were 57% males. The mean HCFs were 41.5%, 53.5%, 42.4%, and 62.6%, for CC depths of 50 mm, 60 mm, 1/4 of EAPD, and 1/3 of EAPD respectively. HCF was significantly lower in male patients for all CC depths. Advanced age and higher BMI showed significant correlation with lower HCF for CC depths of 50 mm and 60 mm. ConclusionThe CC depth measure of 50–60 mm demonstrated efficacy in maintaining HCF and coronary perfusion in the general population except for geriatric and obese individuals. Adjusting CC depth to 1/4–1/3 of the EAPD yielded better outcomes. Future research should prioritize determining individualized CC depths based on EAPD proportion.

Age and chest computed tomography severity score are predictors of long-COVID.

About one-third of acute coronavirus disease 2019 (COVID-19) survivors have suffered from persisting symptoms called long-COVID. Clinical factors such as age and intensity (moderate or acute) of COVID-19 have been found to be associated with long-COVID. Many tissues might be damaged functionally or structurally during acute COVID-19 which can be detected by blood assays and chest computed tomography (CT). We aimed to evaluate the relationship between long-COVID and the initial findings of blood assays and chest CT as possible predictors. The study included patients with acute COVID-19. Laboratory tests and chest CT were obtained from each patient at the time of admission to the hospital. Chest CT was evaluated for pneumonic involvement and severity score. Multivariable regression model was created to find the factors that were independently associated with long-COVID. There were 60 (38.2%) patients with long-COVID and 97 (61.8%) without. Baseline demographic, laboratory and chest CT parameters were similar in both groups, except for age, chronic lung disease and chest CT severity score (46.9 ± 15.1 years vs 52.6 ± 15.9 years, p = 0.03; 11.7% vs 3.1%, p = 0.03 and 10.3 ± 9.6 vs 6.5 ± 7.6, p = 0.02, respectively). In multivariable model, chest CT severity score (OR: 1.059, 95% CI: 1.002-1.119, p = 0.04) and age (OR: 0.953, 95% CI: 0.928-0.979, p < 0.001) were independently associated with long-COVID. Chest CT severity score and age were independently associated with long-COVID and may be used to predict the future risk of long-COVID.

Impact of Elexacaftor-Tezacaftor-Ivacaftor on lung disease in cystic fibrosis.

In people with cystic fibrosis (pwCF), the impact of cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies, such as Elexacaftor-Tezacaftor-Ivacaftor (ETI), on structural changes in the lungs is unclear. To determine the impact of ETI on clinical parameters and on structural lung disease as measured by the changes in the chest computed tomography (CT) scans in pwCF. Percent predicted forced expiratory volume in one second(ppFEV1), body mass index (BMI), and microbiologic data were collected at initiation and 3-month intervals for 1 year. Chest CT scans before starting ETI therapy (baseline) and at 1-year on ETI therapy were compared by two pulmonologists independently. The sample size was 67 pwCF, 30 (44.8%) males, median age of 25 (16, 33.5) years. Significant increases in ppFEV1 and BMI observed by 3 months of ETI therapy persisted throughout 1 year of ETI therapy (p < 0.001 at all-time points for both). After 1 year on ETI, pwCF had significant reductions in Pseudomonas aeruginosa (-42%) and MRSA (-42%) positivity. None of the pwCF had worsening of chest CT parameters during 1 year of ETI therapy. Comparing chest CT findings at baseline and at 1-year follow-up, bronchiectasis was present in 65 (97%) pwCF and at 1-year follow-up decreased in 7 (11%). Bronchial wall thickening 64 (97%), decreased in 53 (79%). Mucous plugging in 63 (96%), absent in 11 (17%), and decreased in 50 (77%). Hyperinflation/air trapping in 44 (67%), decreased in 11 (18%), absent in 27 (44%) CONCLUSIONS: ETI significantly improved clinical outcomes and lung disease as documented by improvement in chest CT scans.

A Novel Prognostic Score Based on Multiple Quantitative Parameters of Chest CT for Patients with Synchronous Multiple Primary Lung Cancer: Is Solid Component Size a Better Prognostic Indicator?

There is no simple and definitive way to predict the prognosis of synchronous multiple primary lung cancer (SMPLC). In this study, we developed a clinical prognostic score for predicting the survival of patients with SMPLC. This study included 206 patients with SMPLC between 2011 and 2020 at three hospitals. Kaplan-Meier analysis was used to determine the optimal cutoff values for the quantitative chest computed tomography (CT) parameters. Multivariable Cox proportional hazards regression was carried out to identify independent prognostic factors for predicting overall survival (OS) and disease-free survival (DFS). The time-dependent receiver operating characteristic curve was analyzed to evaluate the prognostic performance. A CT-based prognostic score (CTPS) comprising six chest CT parameters was developed. Compared with T stage, CTPS had a higher prediction accuracy for OS and DFS. All C-indices of the model reached a satisfactory level in both the development and validation cohorts. Significant differences in the OS and DFS curves were observed when the patients were stratified into different risk groups. The high-risk group (CTPS of 5-6) had poorer survival than the low-risk group (CTPS of 0-4). The developed CTPS and the corresponding risk stratification system are valid for predicting the survival of patients with SMPLC.

Development of a Machine Learning-Assisted Model for the Early Detection of Severe COVID-19 Cases Combining Blood Test and Quantitative Computed Tomography Parameters

&lt;sec&gt; &lt;title&gt;Purpose:&lt;/title&gt; This study aimed to identify severe Coronavirus Disease 2019 (COVID-19) cases combining blood test results and imaging parameters based on a machine learning classifier at the initial admission. &lt;/sec&gt; &lt;sec&gt; &lt;title&gt;Materials and methods:&lt;/title&gt; Ninety-five non-severe and 22 severe laboratory-confirmed COVID-19 cases treated between January 23, 2020 and March 25, 2020 were examined in this retrospective trial. Blood test results and chest computed tomography (CT) images were obtained at the initial admission. The lesions on CT images were segmented using an artificial intelligent (AI) tool. Then, quantitative CT (QCT) parameters, including the volume, percentage, ground glass opacity (GGO) percentage and heterogeneity of the lesions were calculated. Correlations of blood test results and QCT parameters were analyzed by the Pearson test first. Then, discriminative features for detecting severe cases were selected by both the independent samples t test and least absolute shrinkage and selection operator (LASSO) regression. Next, support vector machine (SVM), Gaussian naïve Bayes (GNB), Knearest neighbor (KNN), decision tree (DT), random forest (RF) and multi-layer perceptron-neural net (MLP-NN) algorithms were used as classifiers, and their accuracies were assessed by 10-fold-cross-validation. &lt;/sec&gt; &lt;sec&gt; &lt;title&gt;Results:&lt;/title&gt; Blood test indexes and CT parameters were moderately to medially correlated. Of all selected features, lesion percentage contributed mostly to the classification of the two groups, followed by lesion volume, patient age, lymphocyte count, neutrophil count, GGO percentage and tumor heterogeneity. RF-assisted identification had the highest accuracy of 91.38%, followed by GNB (87.83%), KNN (87.93%), SVM (86.21%), MLP-NN (85.34%) and DT (84.48%). &lt;/sec&gt; &lt;sec&gt; &lt;title&gt;Conclusions:&lt;/title&gt; The RF-assisted model combining blood test and QCT parameters is helpful in the identification of severe COVID-19 cases. &lt;/sec&gt;

Risk Assessment Using Early Quantitative Chest CT Parameters for the Severity of COVID-19

Background: Today, the outbreak of coronavirus disease 2019 (COVID-19) is known as a public health emergency by the World Health Organization (WHO). Therefore, risk assessment is necessary for making a correct decision in disease management. Objectives: This study aimed to assess the risk of progression to the critical stage in COVID-19 patients, based on the early quantitative chest computed tomography (CT) parameters. Patients and Methods: In this case-control study, 39 laboratory-confirmed critical or expired COVID-19 cases (critical group), as well as 117 laboratory-confirmed COVID-19 patients including mild, moderate, and severe cases (non-critical group), were enrolled. Seven quantitative CT parameters, representing the lung volume percentages at different density intervals, were automatically calculated, using the artificial intelligence (AI) algorithms. Multivariable-adjusted logistic regression models, based on the quantitative CT parameters, were established to predict the adverse outcomes (critical vs. non-critical). The predictive performance was estimated using the receiver operating characteristic (ROC) curve analysis and by measuring the area under the ROC curve (AUC). The quantitative CT parameters in different stages were compared between the two groups. Results: No significant differences were found between the two groups regarding the lung volume percentages at different density intervals within 0 - 4 days (P = 0.596-0.938); however, this difference began to become significant within 5 - 9 days and persisted even after one month. Overall, the quantitative CT parameters could well predict the severity of COVID-19. The lung volume percentage of -7 Hounsfield units (-7 HUs) had the largest crude odds ratio (OR: 1.999; 95% CI, 1.453 ~ 2.750; P &lt; 0.001) and adjusted OR (adjusted OR: 1.768; 95% CI, 1.114 ~ 2.808; P = 0.016). The lung volume percentage of -6 HU showed the best predictive performance with the largest AUC of 0.808; the cutoff value of 5.93% showed 71.79% sensitivity and 84.62% specificity. Conclusion: Early quantitative chest CT parameters can be measured to assess the risk of progression to the critical stage of COVID-19; this is of critical importance in the clinical management of this disease.

Long-term chest CT follow-up in COVID-19 Survivors: 102-361 days after onset.

BackgroundThe aim of this study was to evaluate long-term longitudinal changes in chest computed tomography (CT) findings in coronavirus disease 2019 (COVID-19) survivors and their correlations with dyspnea after discharge.MethodsA total of 337 COVID-19 survivors who underwent CT scan during hospitalization and between 102 and 361 days after onset were retrospectively included. Subjective CT findings, lesion volume, therapeutic measures and laboratory parameters were collected. The severity of the survivors' dyspnea was determined by follow-up questionnaire. The evolution of the CT findings from the peak period to discharge and throughout follow-up and the abilities of CT findings and clinical parameters to predict survival with and without dyspnea were analyzed.ResultsNinety-one COVID-19 survivors still had dyspnea at follow-up. The age, comorbidity score, duration of hospital stays, receipt of hormone administration, receipt of immunoglobulin injections, intensive care unit (ICU) admission, receipt of mechanical ventilation, laboratory parameters, clinical classifications and parameters associated with lesion volume of the survivors with dyspnea were significantly different from those of survivors without dyspnea. Among the clinical parameters and CT parameters used to identify dyspnea, parameters associated with lesion volume showed the largest area under the curve (AUC) values, with lesion volume at discharge showing the largest AUC (0.820). Lesion volume decreased gradually from the peak period to discharge and through follow-up, with a notable decrease observed after discharge. Absorption of lesions continued 6 months after discharge.ConclusionsAmong the clinical parameters and subjective CT findings, CT findings associated with lesion volume were the best predictors of post-discharge dyspnea in COVID-19 survivors.

Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results.

PurposeTo evaluate whether early chest computed tomography (CT) lesions quantified by an artificial intelligence (AI)-based commercial software and blood test values at the initial presentation can differentiate the severity of COVID-19 pneumonia.Materials and methodsThis retrospective study included 100 SARS-CoV-2-positive patients with mild (n = 23), moderate (n = 37) or severe (n = 40) pneumonia classified according to the Japanese guidelines. Univariate Kruskal–Wallis and multivariate ordinal logistic analyses were used to examine whether CT parameters (opacity score, volume of opacity, % opacity, volume of high opacity, % high opacity and mean HU total on CT) as well as blood test parameters [procalcitonin, estimated glomerular filtration rate (eGFR), C-reactive protein, % lymphocyte, ferritin, aspartate aminotransferase, lactate dehydrogenase, alanine aminotransferase, creatine kinase, hemoglobin A1c, prothrombin time, activated partial prothrombin time (APTT), white blood cell count and creatinine] differed by disease severity.ResultsAll CT parameters and all blood test parameters except procalcitonin and APPT were significantly different among mild, moderate and severe groups. By multivariate analysis, mean HU total and eGFR were two independent factors associated with severity (p < 0.0001). Cutoff values for mean HU total and eGFR were, respectively, − 801 HU and 77 ml/min/1.73 m2 between mild and moderate pneumonia and − 704 HU and 53 ml/min/1.73 m2 between moderate and severe pneumonia.ConclusionThe mean HU total of the whole lung, determined by the AI algorithm, and eGFR reflect the severity of COVID-19 pneumonia.

Comparison of Clinical and Chest CT Characteristics of Adult Patients with COVID-19 and Mycoplasma Pneumonia

Background: Early differential diagnosis of coronavirus disease 2019 (COVID-19) and Mycoplasma Pneumonia (MP) are hampered by non-specific symptoms, the lack of rapid responding laboratory measures and the presence of family aggregation. Chest computed tomography (CT) plays a significant role to detect the distribution, density and morphology of lesions caused by either COVID-19 or MP. Objectives: To compare the symptoms, laboratory parameters, and chest CT results of adults with COVID-19 and MP and to assess the use of these findings in the differential diagnosis of these diseases. Patients and Methods: The initial clinical manifestations, laboratory results, and chest CT findings of 45 adult patients with COVID-19 (COVID-19 group) and 55 adult patients with MP (MP group) were reviewed retrospectively. All of the patients were diagnosed in the public hospitals in the epidemic area from 20 January to 28 February 2020. Results: Muscle ache and fatigue were more frequently present in the COVID-19 group (P = 0.009 and 0.029, respectively). Increased procalcitonin levels were more common in the MP group (P = 0.001). The chest CT results indicated that bilateral lung involvement, ground glass opacities, “crazy-paving” patterns, and air bronchogram signs were more common in the COVID-19 group (P &lt; 0.001 for all), respectively. However, single lobe involvement, pulmonary consolidations, lobular central nodules, generalized bronchial wall thickening with luminal stenosis, and bronchial mucus impaction were more common in the MP group (P &lt; 0.001 for all). Receiver operating characteristic analysis of a model established using CT parameters indicated a good or excellent performance in distinguishing COVID-19 from MP. Conclusion: COVID-19 and MP have similar clinical manifestations and laboratory results in early stage. However, the chest CT findings are valuable in the differential diagnosis of these two diseases, particularly in patients from COVID-19 epidemic areas.

Glycemic status affects the severity of coronavirus disease 2019 in patients with diabetes mellitus: an observational study of CT radiological manifestations using an artificial intelligence algorithm

AimsIncreasing evidence suggests that poor glycemic control in diabetic individuals is associated with poor coronavirus disease 2019 (COVID-19) pneumonia outcomes and influences chest computed tomography (CT) manifestations. This study aimed to explore the impact of diabetes mellitus (DM) and glycemic control on chest CT manifestations, acquired using an artificial intelligence (AI)-based quantitative evaluation system, and COVID-19 disease severity and to investigate the association between CT lesions and clinical outcome.MethodsA total of 126 patients with COVID-19 were enrolled in this retrospective study. According to their clinical history of DM and glycosylated hemoglobin (HbA1c) level, the patients were divided into 3 groups: the non-DM group (Group 1); the well-controlled blood glucose (BG) group, with HbA1c < 7% (Group 2); and the poorly controlled BG group, with HbA1c ≥ 7% (Group 3). The chest CT images were analyzed with an AI-based quantitative evaluation system. Three main quantitative CT features representing the percentage of total lung lesion volume (PLV), percentage of ground-glass opacity volume (PGV) and percentage of consolidation volume (PCV) in bilateral lung fields were used to evaluate the severity of pneumonia lesions.ResultsPatients in Group 3 had the highest percentage of severe or critical illness, with 12 (32%) cases, followed by 6 (11%) and 7 (23%) cases in Groups 1 and 2, respectively (p = 0.042). The composite endpoints, including death or using mechanical ventilation or admission to the intensive care unit (ICU), were 3 (5%), 5 (16%) and 10 (26%) in Groups 1, 2 and 3, respectively (p = 0.013). The PLV, PGV and PCV in bilateral lung fields were significantly different among the three groups (all p < 0.001): the median PLVs were 12.5% (Group 3), 3.8% (Group 2) and 2.4% (Group 1); the median PGVs were 10.2% (Group 3), 3.6% (Group 2) and 1.9% (Group 1); and the median PCVs were 1.8% (Group 3), 0.3% (Group 2) and 0.1% (Group 1). In the linear regression analyses, which were adjusted for age, sex, BMI, and comorbidities, HbA1c remained positively associated with PLV (β = 0.401, p < 0.001), PGV (β = 0.364, p = 0.001) and PCV (β = 0.472, p < 0.001); this relationship was also observed between fasting blood glucose (FBG) and the three CT quantitative parameters. In the logistic regression analyses, PLV [OR 1.067 (1.032, 1.103)], PGV [OR 1.076 (1.034, 1.120)] and PCV [OR 1.280 (1.110, 1.476)] levels were independent predictors of the composite endpoints, as well as the areas under the ROC (AUCs) for PLV [AUC 0.796 (0.691, 0.900)], PGV [AUC 0.783 (0.678, 0.889)] and PCV [AUC 0.816 (0.722, 0.911)]; the ORs were still significant for CT lesions after adjusting for age, sex and poorly controlled diabetes.ConclusionsIncreased blood glucose level was correlated with the severity of lung involvement, as evidenced by certain chest CT parameters, and clinical prognosis in diabetic COVID-19 patients. There was a positive correlation between blood glucose level (both HbA1c and FBG) on admission and lung lesions. Moreover, the CT lesion severity by AI quantitative analysis was correlated with clinical outcomes.

Volume and Mass Doubling Time of Lung Adenocarcinoma according to WHO Histologic Classification.

ObjectiveThis study aimed to evaluate the tumor doubling time of invasive lung adenocarcinoma according to the International Association of the Study for Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) histologic classification.Materials and MethodsAmong the 2905 patients with surgically resected lung adenocarcinoma, we retrospectively included 172 patients (mean age, 65.6 ± 9.0 years) who had paired thin-section non-contrast chest computed tomography (CT) scans at least 84 days apart with the same CT parameters, along with 10 patients with squamous cell carcinoma (mean age, 70.9 ± 7.4 years) for comparison. Three-dimensional semiautomatic segmentation of nodules was performed to calculate the volume doubling time (VDT), mass doubling time (MDT), and specific growth rate (SGR) of volume and mass. Multivariate linear regression, one-way analysis of variance, and receiver operating characteristic curve analyses were performed.ResultsThe median VDT and MDT of lung cancers were as follows: acinar, 603.2 and 639.5 days; lepidic, 1140.6 and 970.1 days; solid/micropapillary, 232.7 and 221.8 days; papillary, 599.0 and 624.3 days; invasive mucinous, 440.7 and 438.2 days; and squamous cell carcinoma, 149.1 and 146.1 days, respectively. The adjusted SGR of volume and mass of the solid-/micropapillary-predominant subtypes were significantly shorter than those of the acinar-, lepidic-, and papillary-predominant subtypes. The histologic subtype was independently associated with tumor doubling time. A VDT of 465.2 days and an MDT of 437.5 days yielded areas under the curve of 0.791 and 0.795, respectively, for distinguishing solid-/micropapillary-predominant subtypes from other subtypes of lung adenocarcinoma.ConclusionThe tumor doubling time of invasive lung adenocarcinoma differed according to the IASCL/ATS/ERS histologic classification.

Clinical application of the COVID-19 Reporting and Data System (CO-RADS) in patients with suspected SARS-CoV-2 infection: observational study in an emergency department

• CO-RADS improves the risk classification by 65.8% in patients affected by COVID-19. • CO-RADS combined with RT-PCR presents an AUC of 0.98 for patients with COVID-19. • CO-RADS improves the risk classification by 82.1% in patients not affected by COVID-19.

Multicenter cohort study demonstrates more consolidation in upper lungs on initial CT increases the risk of adverse clinical outcome in COVID-19 patients.

Rationale: Chest computed tomography (CT) has been used for the coronavirus disease 2019 (COVID-19) monitoring. However, the imaging risk factors for poor clinical outcomes remain unclear. In this study, we aimed to assess the imaging characteristics and risk factors associated with adverse composite endpoints in patients with COVID-19 pneumonia.Methods: This retrospective cohort study enrolled patients with laboratory-confirmed COVID-19 from 24 designated hospitals in Jiangsu province, China, between 10 January and 18 February 2020. Clinical and initial CT findings at admission were extracted from medical records. Patients aged < 18 years or without available clinical or CT records were excluded. The composite endpoints were admission to ICU, acute respiratory failure occurrence, or shock during hospitalization. The volume, density, and location of lesions, including ground-glass opacity (GGO) and consolidation, were quantitatively analyzed in each patient. Multivariable logistic regression models were used to identify the risk factors among age and CT parameters associated with the composite endpoints.Results: In this study, 625 laboratory-confirmed COVID-19 patients were enrolled; among them, 179 patients without an initial CT at admission and 25 patients aged < 18 years old were excluded and 421 patients were included in analysis. The median age was 48.0 years and the male proportion was 53% (224/421). During the follow-up period, 64 (15%) patients had a composite endpoint. There was an association of older age (odds ratio [OR], 1.04; 95% confidence interval [CI]: 1.01-1.06; P = 0.003), larger consolidation lesions in the upper lung (Right: OR, 1.13; 95%CI: 1.03-1.25, P =0.01; Left: OR,1.15; 95%CI: 1.01-1.32; P = 0.04) with increased odds of adverse endpoints.Conclusion: There was an association of older age and larger consolidation in upper lungs on admission with higher odds of poor outcomes in patients with COVID-19.

Time to Exhale: Additional Value of Expiratory Chest CT in Chronic Obstructive Pulmonary Disease.

Objectives Diagnostic guidelines for chronic obstructive pulmonary disease (COPD) are based on spirometry and clinical criteria. However, this does not address the pathophysiological complexity of the disease sufficiently. Until now, inspiratory chest computed tomography (CT) has been considered as the preferred imaging method in these patients. We hypothesized that expiratory CT may be superior to demonstrate pathophysiological changes. The aim of this prospective study was to systematically compare lung function tests with quantified CT parameters in inspiration and expiration. Materials and Methods Forty-six patients with diagnosed COPD underwent spirometry, body plethysmography, and dose-optimized CT in maximal inspiration and expiration. Four quantified CT parameters were acquired in inspiration, expiration, and their calculated delta values. These parameters were correlated with seven established lung function parameters. Results For inspiratory scans, a weak-to-moderate correlation with the lung function parameters was found. These correlations significantly improved when adding the expiratory scan (p < 0.05). Moreover, some parameters showed a significant correlation only in expiratory datasets. Calculated delta values showed even stronger correlation with lung function testing. Conclusions Expiratory quantified CT and calculated delta values significantly improve the correlation with lung function parameters. Thus, an additional expiratory CT may improve image-based phenotyping of patients with COPD.

Low-voltage chest CT: another way to reduce the radiation dose in asbestos-exposed patients

To assess whether low voltage chest computed tomography (CT) can be used to successfully diagnose disease in patients with asbestos exposure. Fifty-six former employees of the shipbuilding industry, who were candidates to receive a standard-dose chest CT due to their occupational exposure to asbestos, underwent a routine CT. Immediately after this initial CT, they underwent a second acquisition using low-dose chest CT parameters, based on a low potential (80kV) and limited tube current. The findings of the two CT protocols were compared based on typical diseases associated with asbestos exposure. The kappa coefficient for each parameter and for an overall rating (grouping them based on mediastinal, pleural, and pulmonary findings) were calculated in order to test for correlations between the two protocols. A good correlation between routine and low-dose CT was demonstrated for most parameters with a mean radiation dose reduction of up to 83% of the effective dose based on the dose-length product between protocols. Low-dose chest CT, based on a limited tube potential, is useful for patients with an asbestos exposure background. Low-dose chest CT can be successfully used to minimise the radiation dose received by patients, as this protocol produced an estimated mean effective dose similar to that of an abdominal or pelvis plain film.

The correlation of morphological features of chest computed tomographic scans with clinical characteristics of thymoma.

Chest computed tomography (CT) scanning has been widely utilized in thymoma identification and staging as well as in follow-up monitoring for recurrence. However, the relationship between some CT imaging features and pathological types, clinical stage, completeness of resection, or prognosis in thymoma has not been well explored. We retrospectively reviewed preoperative CT imaging for 238 thymoma patients, who had undergone thymectomy from October 2007 to December 2011. All CT parameters were assessed in each case based on clinical and pathological data. Survival analysis was performed by using the Kaplan-Meier and log-rank tests. Tumour contours (P = 0.008), homogeneity (P = 0.009), degree of enhancement (P = 0.013), fat plane obliteration with adjacent structures (P < 0.001), the presence of mediastinal lymphadenopathy (P = 0.010), irregular infiltration into the lung (P = 0.012) and tumour shape (P = 0.007) were associated with the World Health Organization (WHO) histological classification. Lobulated or irregular tumour contours (P < 0.001), presence of calcifications (P = 0.002), infiltration of surrounding fat (P < 0.001), irregular infiltration into the lung (P < 0.001), irregular infiltration into vascular (P < 0.001), more abutment of vessels (P < 0.001) and pulmonary changes adjacent to the tumour (P < 0.001) were associated with the more advanced Masaoka-Koga clinical stage. Tumour contours (P < 0.001), infiltration of surrounding fat (P = 0.008), irregular infiltration into the lung (P < 0.001) and degree of abutment of vessel circumference (P = 0.001) were associated with completeness of resection. With multivariate analysis, no CT image features could reliably predict on the overall or disease-free survival rate. CT imaging does have some features, which are significantly correlated with the WHO classification, the Masaoka-Koga clinical staging and the completeness of resection, although it has no definite role to evaluate preoperatively the survival rate of thymoma patients.

OP0095 Comparison of Interstitial Lung Disease CT Indexes and Pulmonary Function Values in Sistemic Sclerosis Patients: A Multicenter Study

BackgroundCurrently, interstitial lung disease (ILD) related to systemic sclerosis (SSc) is assessed with pulmonary function tests (PFTs) and chest Computed Tomography (CT). FVC <70% and DLco <70% were proposed as...

Low-dose chest computed tomography for quantitative and visual anatomical analysis in patients with acute respiratory distress syndrome

Chest computed tomography (CT) is a fundamental tool for the characterization of acute respiratory distress syndrome (ARDS). Its frequent use is, however, hindered by the associated radiation exposure. The aim of the present study was to evaluate, in patients with ARDS, the accuracy of quantitative and visual anatomical lung analysis performed on low-dose CT. We hypothesized that low-dose CT would provide accurate quantitative and visual anatomical results. Chest CT was performed in 45 ARDS patients in static conditions at set airway pressures of 45 and 15 or 45 and 5 cmH2O. During each pause, two consecutive scans were obtained at two different tube current-time products (mAs). In 24 patients 110 mAs was coupled with 60 mAs; in 21 patients 110 was coupled with 30 mAs. All other CT parameters were kept unaltered. Quantitative and visual anatomical results obtained at different mAs were compared via Bland-Altman analysis. Good agreements were observed between 110 and 60 mAs and between 110 and 30 mAs both for quantitative and visual anatomical results (all biases below 1.5%). Estimated mean effective dose at 110, 60, and 30 mAs corresponded to 5.3 ± 1.6, 2.8 ± 0.8, and 1.4 ± 0.3 mSv, respectively. In patients with ARDS a reduction of mAs up to 30 (70 % effective dose reduction) can be achieved without significant effect on quantitative and visual anatomical results. Low-dose chest CT, with related quantitative and visual anatomical analysis, could be a valuable tool to characterize and potentially monitor lung disease in patients with ARDS.

Increased Pulmonary Artery Diameter on Chest Computed Tomography Can Predict Borderline Pulmonary Hypertension

Pulmonary hypertension (PH) often leads to dilatation of the pulmonary artery (PA), which can be measured on chest computed tomography (CT). While the predictive capability of PA dilatation is useful to distinguish PH (mean PA pressure ≥25 mmHg) from normal (mean PA pressure ≤ 20 mmHg), CT characteristics of borderline PH (mean PA pressure 21-24 mmHg) have not been described. We aimed to investigate whether patients with borderline PH already show PA dilatation and to assess the diagnostic accuracy of PA dilatation for borderline PH diagnosis. Between April 2003 and September 2008, consecutive symptomatic patients with a mean PA pressure below 25 mmHg on right heart catheterization who had a chest CT available were retrospectively included. PA diameters from chest CT were correlated with hemodynamic measurements and analyzed with respect to their accuracy of predicting borderline PH. Main PA diameters were significantly larger in 26 patients with borderline PH compared with 52 patients without PH (3.16 ± 0.53 vs. 2.78 ± 0.43 cm, P = 0.001). The main PA diameter on CT correlated with mean PA pressure (r = 0.496, P < 0.001) and pulmonary vascular resistance (r = 0.445, P < 0.001), and predicted borderline PH with sensitivity, specificity, negative and positive predictive values of 77%, 62%, 84%, and 50%, respectively, using a cutoff ≥2.9 cm. This first systematic investigation of CT parameters in symptomatic patients with mean PA pressures less than 25 mmHg could show that, even in patients with borderline PH, significant PA dilatation can be present, which was related to PA pressure and pulmonary vascular resistance. This can be useful for identification of patients with borderline PH for further study and to prompt further diagnostic work-up of possible underlying diseases.

Evaluation of right ventricular dysfunction and prediction of clinical outcomes in acute pulmonary embolism by chest computed tomography: comparisons with echocardiography

To evaluate the ability to identify right ventricular (RV) dysfunction, and to predict adverse outcomes of chest computed tomography (CT), we compared CT and echocardiography in acute pulmonary embolism patients. We analyzed 56 patients diagnosed by CT with acute pulmonary embolism, who underwent echocardiography within 48 h of CT scan from January 2004 to December 2008. From the CT scan, the ratio of RV diameter to left ventricular diameter (RVd/LVd), the presence of septal bowing and embolus location were determined. RVd/LVd (P < 0.001), septal bowing (P < 0.001) and proximal embolism (P = 0.016) were associated with echocardiographic RV hypokinesia. The odds ratio for adverse clinical outcomes was 19.2 for the combination of three CT parameters (RVd/LVd > 1, septal bowing, and proximal embolism), and 13.4 for RV hypokinesia (each P = 0.001). The positive predictive value (PPV) for adverse clinical outcomes for echocardiographic RV hypokinesia was 55.0%, and the negative predictive value (NPV) was 96.2%. The three-parameter combination predicted adverse clinical outcomes with a PPV of 54.5%, and a NPV of 94.1%. CT parameters including RV dysfunction were significantly associated with poor outcomes. Rapid risk stratification of patients with acute pulmonary embolism based on chest CT appears to be comparable with echocardiography, is clinically reliable, and may be useful in guiding management strategy.