Pneumonia pattern recognition on ultra-low-dose CT does not allow for a reliable differentiation between viral and bacterial pneumonia: A multicentre observer study.

PURPOSE: While a reliable differentiation between viral and bacterial pneumonia is not possible with chest X-ray, this study investigates whether ultra-low-dose chest-CT (ULDCT) could be used for this purpose. METHODS: In the OPTIMACT trial 281 patients had a final diagnosis of pneumonia, and 96/281 (34%) had one or more positive microbiology results: 60 patients viral pathogens, 48 patients bacterial pathogens. These 96 ULDCT's were blindly and independently evaluated by two chest radiologists, who reported CT findings, pneumonia pattern, and most likely type of pathogen. Differences between groups were analysed for each radiologist separately, diagnostic accuracy was evaluated by calculating sensitivity. RESULTS: The dominant CT finding significantly differed between the viral and bacterial pathogen groups (p = 0.04; p = 0.04). Consolidation was the most frequent dominant CT finding in both patients with viral and bacterial pathogens, but was observed significantly more often in those with a bacterial pathogen: 32/60 and 22/60 versus 38/48 and 31/48 (p = 0.005; p = 0.004). The lobar pneumonia pattern was more frequently observed in patients with a bacterial pathogen: 23/48 and 18/48, versus 10/60 and 8/60 for viral pathogens (p < 0.001; p = 0.004). For the bronchopneumonia and interstitial pneumonia patterns the proportions of viral and bacterial pathogens were not significantly different. Both radiologists suggested a viral pathogen correctly (sensitivity) in 6/60 (10%), for a bacterial pathogen this was 34/48 (71%). CONCLUSION: Reliable differentiation between viral and bacterial pneumonia could not be made by pattern recognition on ULDCT, although a lobar pneumonia pattern was significantly more often observed in bacterial infection.

Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers.

PURPOSE: To compare human observers to a mathematically derived computer model for differentiation between malignant and benign pulmonary nodules detected on baseline screening computed tomography (CT) scans. METHODS: A case-cohort study design was chosen. The study group consisted of 300 chest CT scans from the Danish Lung Cancer Screening Trial (DLCST). It included all scans with proven malignancies (n = 62) and two subsets of randomly selected baseline scans with benign nodules of all sizes (n = 120) and matched in size to the cancers, respectively (n = 118). Eleven observers and the computer model (PanCan) assigned a malignancy probability score to each nodule. Performances were expressed by area under the ROC curve (AUC). Performance differences were tested using the Dorfman, Berbaum and Metz method. Seven observers assessed morphological nodule characteristics using a predefined list. Differences in morphological features between malignant and size-matched benign nodules were analyzed using chi-square analysis with Bonferroni correction. A significant difference was defined at p < 0.004. RESULTS: Performances of the model and observers were equivalent (AUC 0.932 versus 0.910, p = 0.184) for risk-assessment of malignant and benign nodules of all sizes. However, human readers performed superior to the computer model for differentiating malignant nodules from size-matched benign nodules (AUC 0.819 versus 0.706, p < 0.001). Large variations between observers were seen for ROC areas and ranges of risk scores. Morphological findings indicative of malignancy referred to border characteristics (spiculation, p < 0.001) and perinodular architectural deformation (distortion of surrounding lung parenchyma architecture, p < 0.001; pleural retraction, p = 0.002). CONCLUSIONS: Computer model and human observers perform equivalent for differentiating malignant from randomly selected benign nodules, confirming the high potential of computer models for nodule risk estimation in population based screening studies. However, computer models highly rely on size as discriminator. Incorporation of other morphological criteria used by human observers to superiorly discriminate size-matched malignant from benign nodules, will further improve computer performance.

Arterial Stiffness Is Positively Associated With 18F-fluorodeoxyglucose Positron Emission Tomography-Assessed Subclinical Vascular Inflammation in People With Early Type 2 Diabetes.

OBJECTIVE: Type 2 diabetes is accompanied by premature atherosclerosis and arterial stiffness. The underlying association remains incompletely understood. The possible relationship between subclinical arterial inflammation assessed by (18)F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and arterial stiffness was investigated in patients with early type 2 diabetes. RESEARCH DESIGN AND METHODS: Patients with type 2 diabetes (n = 44), without cardiovascular disease and any type of antidiabetic medication, were studied (median age 63 years [interquartile range 54-66], men:women 27:17). Arterial inflammation was quantified as the FDG uptake maximal standardized uptake value (SUVmax). SUVmax was corrected for the prescan glucose level. A target-to-background ratio (TBR) was calculated by dividing the SUVmax of the arteries by the SUVmean of the caval veins (blood pool). TBRs were calculated for four individual segments (carotid arteries, ascending aorta and aortic arch, descending and abdominal aorta, and iliac and femoral arteries) and averaged for the total aortic tree (meanTBR). Arterial stiffness was assessed as central systolic blood pressure (cSBP), carotid-femoral pulse wave velocity (PWV), and augmentation index (AIx). RESULTS: The meanTBR was significantly associated with PWV (R = 0.47, P = 0.001) and cSBP (R = 0.45, P = 0.003) but not with AIx. TBR of each separate segment was also significantly associated with PWV and cSBP. In a multiple linear regression model including age, sex, BMI, hemoglobin A1c (HbA1c), hs-CRP, cholesterol, cSBP, and PWV, PWV was the strongest determinant of meanTBR. CONCLUSIONS: In patients with type 2 diabetes, FDG-PET/CT-imaged subclinical arterial inflammation is positively associated with determinants of arterial stiffness.