Journal of Thoracic Imaging最新文献

Purpose: Apical pleuroparenchymal scarring (APPS) is commonly seen on chest computed tomography (CT), though the imaging and clinical features, to the best of our knowledge, have never been studied. The purpose was to understand APPS's typical morphologic appearance and associated clinical features.

Patients and methods: A random generator selected 1000 adult patients from all 21516 chest CTs performed at urban outpatient centers from January 1, 2016 to December 31, 2016. Patients with obscuring apical diseases were excluded to eliminate confounding factors. After exclusions, 780 patients (median age: 64 y; interquartile range: 56 to 72 y; 55% males) were included for analysis. Two radiologists evaluated the lung apices of each CT for the extent of abnormality in the axial plane (mild: <5 mm, moderate: 5 to 10 mm, severe: >10 mm), craniocaudal plane (extension halfway to the aortic arch, more than halfway, vs below the arch), the predominant pattern (nodular vs reticular and symmetry), and progression. Cohen kappa coefficient was used to assess radiologists' agreement in scoring. Ordinal logistic regression was used to determine associations of clinical and imaging variables with APPS.

Results: APPS was present on 65% (507/780) of chest CTs (54% mild axial; 80% mild craniocaudal). The predominant pattern was nodular and symmetric. Greater age, female sex, lower body mass index, greater height, and white race were associated with more extensive APPS. APPS was not found to be associated with lung cancer in this cohort.

Conclusion: Classifying APPS by the extent of disease in the axial or craniocaudal planes, in addition to the predominant pattern, enabled statistically significant associations to be determined, which may aid in understanding the pathophysiology of apical scarring and potential associated risks.

Purpose: The aim of this study was to explore the association of cardiac CT-based left atrium (LA) structural and functional parameters and left atrial epicardial adipose tissue (LA-EAT) parameters with postablation atrial fibrillation (AF) recurrence within 2 years.

Materials and methods: Contrast-enhanced cardiac CT images of 286 consecutive AF patients (median age: 65 y; 97 females) who underwent initial ablation between June 2018 and June 2020 were retrospectively analyzed. Structural and functional parameters of LA, including maximum and minimum volume and ejection fraction of LA and left atrial appendage (LAA), and LA-EAT volume, were measured. The body surface area indexed maximum and minimum volume of LA (LAVImax, LAVImin) and LAA (LAAVImax, LAAVImin), and LA-EAT volume index (LA-EATVI) were calculated. Independent predictors of AF recurrence were determined using Cox regression analysis. The clinical predictors were added to the imaging predictors to build a combined model (clinical+imaging). The predictive performance of the clinical, imaging, and combined models was assessed using the area under the receiver operating characteristics curve (AUC).

Results: A total of 108 (37.8%) patients recurred AF within 2 years after ablation at a median follow-up of 24 months (IQR=11, 32). LA and LAA size and LA-EAT volume were significantly increased in patients with AF recurrence (P<0.05). After the multivariable regression analysis, LA-EATVI, LAAVImax, female sex, AF duration, and stroke history were independent predictors for AF recurrence. The combined model exhibited superior predictive performance compare to the clinical model (AUC=0.712 vs. 0.641, P=0.023) and the imaging model (AUC=0.712 vs. 0.663, P=0.018).

Conclusion: Cardiac CT-based LA-EATVI and LAAVImax are independent predictors for postablation AF recurrence within 2 years and may provide a complementary value for AF recurrence risk assessment.

Purpose: We investigated spatial resolution loss away from isocenter for a prototype deep silicon photon-counting detector (PCD) CT scanner and compare with a clinical energy-integrating detector (EID) CT scanner.

Materials and methods: We performed three scans on a wire phantom at four positions (isocenter, 6.7, 11.8, and 17.1 cm off isocenter). The acquisition modes were 120 kV EID CT, 120 kV high-definition (HD) EID CT, and 120 kV PCD CT. HD mode used double the projection view angles per rotation as the "regular" EID scan mode. The diameter of the wire was calculated by taking the full width of half max (FWHM) of a profile drawn over the radial and azimuthal directions of the wire. Change in wire diameter appearance was assessed by calculating the ratio of the radial and azimuthal diameter relative to isocenter. t tests were used to make pairwise comparisons of the wire diameter ratio with each acquisition and mean ratios' difference from unity.

Results: Deep silicon PCD CT had statistically smaller (P<0.05) changes in diameter ratio for both radial and azimuthal directions compared with both regular and HD EID modes and was not statistically different from unity (P<0.05). Maximum increases in FWMH relative to isocenter were 36%, 12%, and 1% for regular EID, HD EID, and deep silicon PCD, respectively.

Conclusion: Deep silicon PCD CT exhibits less change in spatial resolution in both the radial and azimuthal directions compared with EID CT.

Purpose: To study the performance of artificial intelligence (AI) for detecting pleural pathology on chest radiographs (CXRs) using computed tomography as ground truth.

Patients and methods: Retrospective study of subjects undergoing CXR in various clinical settings. Computed tomography obtained within 24 hours of the CXR was used to volumetrically quantify pleural effusions (PEfs) and pneumothoraxes (Ptxs). CXR was evaluated by AI software (INSIGHT CXR; Lunit) and by 3 second-year radiology residents, followed by AI-assisted reassessment after a 3-month washout period. We used the area under the receiver operating characteristics curve (AUROC) to assess AI versus residents' performance and mixed-model analyses to investigate differences in reading time and interreader concordance.

Results: There were 96 control subjects, 165 with PEf, and 101 with Ptx. AI-AUROC was noninferior to aggregate resident-AUROC for PEf (0.82 vs 0.86, P < 0.001) and Ptx (0.80 vs 0.84, P = 0.001) detection. AI-assisted resident-AUROC was higher but not significantly different from the baseline. AI-assisted reading time was reduced by 49% (157 vs 80 s per case, P = 0.009), and Fleiss kappa for Ptx detection increased from 0.70 to 0.78 ( P = 0.003). AI decreased detection error for PEf (odds ratio = 0.74, P = 0.024) and Ptx (odds ratio = 0.39, P < 0.001).

Conclusion: Current AI technology for the detection of PEf and Ptx on CXR was noninferior to second-year resident performance and could help decrease reading time and detection error.

Purpose: Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests.

Materials and methods: Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient ( r ) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO).

Results: We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC ( P = 0.001) and FVC ( P = 0.04) values for the higher PPV patients, but not for DLCO ( P = 0.19).

Conclusion: We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.

Purpose: Shortened injection durations are not recommended in step-and-shoot coronary computed tomography angiography (CCTA). We aimed to evaluate the image quality of CCTA performed using bodyweight-adjusted iodinated contrast media (ICM) with different injection durations to generate an optimized ICM administration protocol to acquire convincible image quality in step-and-shoot CCTA.

Materials and methods: A total of 200 consecutive patients with suspected coronary artery disease (CAD) were enrolled in group A (N=50, 350 mgI/mL, bodyweight×0.8 mL/kg with a 13-s injection duration), group B (N=50, 350 mgI/mL, bodyweight×0.9 mL/kg with a 13-s injection duration), group C (N=50, 350 mgI/mL, bodyweight×0.8 mL/kg with a 12-s injection duration), and group D (N=50, 320 mgI/mL, bodyweight×0.8 mL/kg with a 13-s injection duration). Patient characteristics, ICM administration protocols, quantitative computed tomography (CT) value measurements, and qualitative image scores were analyzed and compared among the groups.

Results: Groups A and D achieved the lowest ICM volume, saline volume, injection flow rate, and total iodine and iodine injection rates among the groups. All the CT values of the coronary arteries in all groups were >300 HU. All the observers' average scores exceeded three points. In group A, the CT values showed significant positive correlation with the iodine injection rate ( r =0.226, P <0.001), whereas the signal-to-noise ratio ( r =-0.004, P =0.927) and contrast-to-noise ratio ( r =-0.006, P =0.893) values were not.

Conclusions: Bodyweight×0.8 mL/kg with a 13-second injection duration is a comprehensive option for step-and-shoot CCTA with improved image quality, and a 350 mgI/mL iodine concentration is preferred.

Purpose: Spectral computed tomography (CT) enables improved tissue characterization, although virtually all research has focused on contrast-enhanced examinations. We hypothesized that changes in myocardial tissue related to acute myocardial infarction (AMI) might potentially be identified without the need for contrast administration using electron density (ED) imaging.

Patients and methods: This retrospective observational study involved a small series (n = 15) of patients admitted to our institution with a first AMI without signs of hemodynamic instability and identification of a culprit vessel with invasive coronary angiography during the same admission, who also underwent a noncontrast, low-dose chest CT using a dual-layer spectral CT scanner. Images were assessed in search of dark areas with low density on ED imaging, and the mean percentage ED relative to water (%EDW) was calculated.

Results: Using a qualitative approach, ED assessment enabled the identification of 11/15 (73%) affected coronary territories, with a sensitivity of 73% (95% CI: 45; 92%) and a specificity of 87% (95% CI: 69; 96%). AMI segments showed significantly lower ED values than the remote myocardium (103.8 ± 0.8 vs 104.3 ± 0.6 %EDW, P < 0.0001), and a threshold below 103.9 %EDW had a sensitivity of 66% and specificity of 79% for the identification of AMI. In a control group of patients without a history of cardiovascular disease, none had areas with focal reduction of ED following the shape of the myocardial wall.

Conclusions: In our preliminary series, ED imaging showed the potential to enable the identification of myocardial tissue changes related to AMI without iodinated contrast requirement.

Purpose: Pericardial fat (PF) and epicardial adipose tissue (EAT) may enhance the proinflammatory response in corona virus-19 (COVID-19) patients. Higher PF and EAT volumes might result in multiorgan failure and explain unfavorable trajectories.The aim of this study was to examine the association between the volume of PF and EAT and multiorgan failure over time.

Materials and methods: All mechanically ventilated COVID-19 patients with an available chest computed tomography were prospectively included (March-June 2020). PF and EAT volumes were quantified using chest computed tomography scans. Patients were categorized into sex-specific PF and EAT tertiles. Variables to calculate Sequential Organ Failure Assessment (SOFA) scores were collected daily to indicate multiorgan failure. Linear mixed-effects regression was used to investigate the association between tertiles for PF and EAT volumes separately and serial SOFA scores over time. All models were adjusted.

Results: Sixty-three patients were divided into PF and EAT tertiles, with median PF volumes of 131.4 mL (IQR [interquartile range]: 115.7, 143.2 mL), 199.8 mL (IQR: 175.9, 221.6 mL), and 318.8 mL (IQR: 281.9, 376.8 mL) and median EAT volumes of 69.6 mL (IQR: 57.0, 79.4 mL), 107.9 mL (IQR: 104.6, 115.1 mL), and 163.8 mL (IQR: 146.5, 203.1 mL). Patients in the highest PF tertile had a statistically significantly lower SOFA score over time (1.3 [-2.5, -0.1], P =0.033) compared with the lowest PF tertile. EAT tertiles were not significantly associated with SOFA scores over time.

Conclusion: A higher PF volume is associated with less multiorgan failure in mechanically ventilated COVID-19 patients. EAT volumes were not associated with multiorgan failure.

Objective: Noninvasive measurement of myocardial work (MW) incorporates left ventricular (LV) pressure, and, therefore, allows correction of global longitudinal strain for changing afterload conditions. We sought to investigate MW as a tool to detect early signs of LV dysfunction in primary systemic hypertension patients, particularly with different predictive indices.

Methods and results: None left ventricular hypertrophy (NLVH) and left ventricular hypertrophy (LVH) patients established were all primary systemic hypertension with preserved ejection fraction. Forty in NLVH and forty in LVH according to left ventricular end-diastolic mass index (LVEDmassI) were prospectively enrolled. The following indices of MW were assessed: global work index, global constructive work, global wasted work (GWW), and global work efficiency (GWE). Both global work index ( P =0.348) and global constructive work ( P =0.225) were increased in NLVH and decreased in LVH, and GWW ( P <0.001) was increased significantly in NLVH and increased more in LVH, while GWE ( P <0.001) was decreased significantly in NLVH and decreased more in LVH. The clinical utility of GWW (95% CI: 0.802-0.951) and GWE (95% CI: 0.811-0.950) were verified by receiver-operating characteristic curve analysis showing larger net benefits as evaluated with LVH and control comparisons. In multivariate linear regression analysis, 4-dimenaional LVEDmassI was independently associated with GWE ( P =0.018) in systemic hypertension patients. Assessment of intraobserver and interobserver variability in the MW echocardiographic data documented good interclass correlation coefficients (all >0.85).

Conclusion: GWW and GWE derived from MW are more accurate, sensitive, and reproducible predictors to detect early LV dysfunction in primary systemic hypertension patients, especially in distinguishing the potential functional abnormality of NLVH and LVH, even though the ejection fraction is preserved.