Galectin-10 in serum extracellular vesicles reflects asthma pathophysiology.

BACKGROUND: Novel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes. OBJECTIVE: We sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs). METHODS: We performed data-independent acquisition of serum EVs from 4 healthy controls, 4 noneosinophilic asthma (NEA) patients, and 4 eosinophilic asthma (EA) patients to identify novel BMs for BA. We confirmed EA-specific BMs via data-independent acquisition validation in 61 BA patients and 23 controls. To further validate these findings, we performed data-independent acquisition for 6 patients with chronic rhinosinusitis without nasal polyps and 7 patients with chronic rhinosinusitis with nasal polyps. RESULTS: We identified 3032 proteins, 23 of which exhibited differential expression in EA. Ingenuity pathway analysis revealed that protein signatures from each phenotype reflected disease characteristics. Validation revealed 5 EA-specific BMs, including galectin-10 (Gal10), eosinophil peroxidase, major basic protein, eosinophil-derived neurotoxin, and arachidonate 15-lipoxygenase. The potential of Gal10 in EVs was superior to that of eosinophils in terms of diagnostic capability and detection of airway obstruction. In rhinosinusitis patients, 1752 and 8413 proteins were identified from EVs and tissues, respectively. Among 11 BMs identified in EVs and tissues from patients with chronic rhinosinusitis with nasal polyps, 5 (including Gal10 and eosinophil peroxidase) showed significant correlations between EVs and tissues. Gal10 release from EVs was implicated in eosinophil extracellular trapped cell death in vitro and in vivo. CONCLUSION: Novel BMs such as Gal10 from serum EVs reflect disease pathophysiology in BA and may represent a new target for liquid biopsy approaches.

Pictorial Review of Pleural Disease: Multimodality Imaging and Differential Diagnosis.

The pleura is a thin, smooth, soft-tissue structure that lines the pleural cavity and separates the lungs from the chest wall, consisting of the visceral and parietal pleurae and physiologic pleural fluid. There is a broad spectrum of normal variations and abnormalities in the pleura, including pneumothorax, pleural effusion, and pleural thickening. Pneumothorax is associated with pulmonary diseases and is caused by iatrogenic or traumatic factors. Chest radiography and US help detect pneumothorax with various signs, and CT can also help assess the causes. Pleural effusion occurs in a wide spectrum of diseases, such as heart failure, cirrhosis, asbestos-related diseases, infections, chylothorax, and malignancies. Chest US allows detection of a small pleural effusion and evaluation of echogenicity or septa in pleural effusion. Pleural thickening may manifest as unilateral or bilateral and as focal, multifocal, or diffuse. Various diseases can demonstrate pleural thickening, such as asbestos-related diseases, neoplasms, and systemic diseases. CT, MRI, and fluorodeoxyglucose (FDG) PET/CT can help differentiate between benign and malignant lesions. Knowledge of these features can aid radiologists in suggesting diagnoses and recommending further examinations with other imaging modalities. The authors provide a comprehensive review of the clinical and multimodality imaging findings of pleural diseases and their differential diagnoses. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Prevalence and mortality associations of interstitial lung abnormalities in rheumatoid arthritis within a multicentre prospective cohort of smokers.

OBJECTIVE: To investigate the prevalence and mortality impact of interstitial lung abnormalities (ILAs) in RA and non-RA comparators. METHODS: We analysed associations between ILAs, RA, and mortality in COPDGene, a multicentre prospective cohort study of current and past smokers, excluding known interstitial lung disease (ILD) or bronchiectasis. All participants had research chest high-resolution CT (HRCT) reviewed by a sequential reading method to classify ILA as present, indeterminate or absent. RA cases were identified by self-report RA and DMARD use; non-RA comparators had neither an RA diagnosis nor used DMARDs. We examined the association and mortality risk of RA and ILA using multivariable logistic regression and Cox regression. RESULTS: We identified 83 RA cases and 8725 non-RA comparators with HRCT performed for research purposes. ILA prevalence was 16.9% in RA cases and 5.0% in non-RA comparators. After adjusting for potential confounders, including genetics, current/past smoking and other lifestyle factors, ILAs were more common among those with RA compared with non-RA [odds ratio 4.76 (95% CI 2.54, 8.92)]. RA with ILAs or indeterminate for ILAs was associated with higher all-cause mortality compared with non-RA without ILAs [hazard ratio (HR) 3.16 (95% CI 2.11, 4.74)] and RA cases without ILA [HR 3.02 (95% CI 1.36, 6.75)]. CONCLUSIONS: In this cohort of smokers, RA was associated with ILAs and this persisted after adjustment for current/past smoking and genetic/lifestyle risk factors. RA with ILAs in smokers had a 3-fold increased all-cause mortality, emphasizing the importance of further screening and treatment strategies for preclinical ILD in RA.

Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.

OBJECTIVES: To compare the performance of radiologists in characterizing and diagnosing pulmonary nodules/masses with and without deep learning (DL)-based computer-aided diagnosis (CAD). METHODS: We studied a total of 101 nodules/masses detected on CT performed between January and March 2018 at Osaka University Hospital (malignancy: 55 cases). SYNAPSE SAI Viewer V1.4 was used to analyze the nodules/masses. In total, 15 independent radiologists were grouped (n = 5 each) according to their experience: L (< 3 years), M (3-5 years), and H (> 5 years). The likelihoods of 15 characteristics, such as cavitation and calcification, and the diagnosis (malignancy) were evaluated by each radiologist with and without CAD, and the assessment time was recorded. The AUCs compared with the reference standard set by two board-certified chest radiologists were analyzed following the multi-reader multi-case method. Furthermore, interobserver agreement was compared using intraclass correlation coefficients (ICCs). RESULTS: The AUCs for ill-defined boundary, irregular margin, irregular shape, calcification, pleural contact, and malignancy in all 15 radiologists, irregular margin and irregular shape in L and ill-defined boundary and irregular margin in M improved significantly (p < 0.05); no significant improvements were found in H. L showed the greatest increase in the AUC for malignancy (not significant). The ICCs improved in all groups and for nearly all items. The median assessment time was not prolonged by CAD. CONCLUSIONS: DL-based CAD helps radiologists, particularly those with < 5 years of experience, to accurately characterize and diagnose pulmonary nodules/masses, and improves the reproducibility of findings among radiologists. KEY POINTS: • Deep learning-based computer-aided diagnosis improves the accuracy of characterizing nodules/masses and diagnosing malignancy, particularly by radiologists with < 5 years of experience. • Computer-aided diagnosis increases not only the accuracy but also the reproducibility of the findings across radiologists.

New trend in artificial intelligence-based assistive technology for thoracic imaging.

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.

Clinical applications of artificial intelligence in liver imaging.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

Traction Bronchiectasis/Bronchiolectasis on CT Scans in Relationship to Clinical Outcomes and Mortality: The COPDGene Study.

Background The clinical impact of interstitial lung abnormalities (ILAs) on poor prognosis has been reported in many studies, but risk stratification in ILA will contribute to clinical practice. Purpose To investigate the association of traction bronchiectasis/bronchiolectasis index (TBI) with mortality and clinical outcomes in individuals with ILA by using the COPDGene cohort. Materials and Methods This study was a secondary analysis of prospectively collected data. Chest CT scans of participants with ILA for traction bronchiectasis/bronchiolectasis were evaluated and outcomes were compared with participants without ILA from the COPDGene study (January 2008 to June 2011). TBI was classified as follows: TBI-0, ILA without traction bronchiectasis/bronchiolectasis; TBI-1, ILA with bronchiolectasis but without bronchiectasis or architectural distortion; TBI-2, ILA with mild to moderate traction bronchiectasis; and TBI-3, ILA with severe traction bronchiectasis and/or honeycombing. Clinical outcomes and overall survival were compared among the TBI groups and the non-ILA group by using multivariable linear regression model and Cox proportional hazards model, respectively. Results Overall, 5295 participants (median age, 59 years; IQR, 52-66 years; 2779 men) were included, and 582 participants with ILA and 4713 participants without ILA were identified. TBI groups were associated with poorer clinical outcomes such as quality of life scores in the multivariable linear regression model (TBI-0: coefficient, 3.2 [95% CI: 0.6, 5.7; P = .01]; TBI-1: coefficient, 3.3 [95% CI: 1.1, 5.6; P = .003]; TBI-2: coefficient, 7.6 [95% CI: 4.0, 11; P < .001]; TBI-3: coefficient, 32 [95% CI: 17, 48; P < .001]). The multivariable Cox model demonstrated that ILA without traction bronchiectasis (TBI-0-1) and with traction bronchiectasis (TBI-2-3) were associated with shorter overall survival (TBI-0-1: hazard ratio [HR], 1.4 [95% CI: 1.0, 1.9; P = .049]; TBI-2-3: HR, 3.8 [95% CI: 2.6, 5.6; P < .001]). Conclusion Traction bronchiectasis/bronchiolectasis was associated with poorer clinical outcomes compared with the group without interstitial lung abnormalities; TBI-2 and 3 were associated with shorter survival. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Im in this issue.

Interstitial lung abnormalities are associated with decreased mean telomere length.

BACKGROUND: Interstitial lung abnormalities (ILA) share many features with idiopathic pulmonary fibrosis; however, it is not known if ILA are associated with decreased mean telomere length (MTL). METHODS: Telomere length was measured with quantitative PCR in the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) and Age Gene/Environment Susceptibility Reykjavik (AGES-Reykjavik) cohorts and Southern blot analysis was used in the Framingham Heart Study (FHS). Logistic and linear regression were used to assess the association between ILA and MTL; Cox proportional hazards models were used to assess the association between MTL and mortality. RESULTS: In all three cohorts, ILA were associated with decreased MTL. In the COPDGene and AGES-Reykjavik cohorts, after adjustment there was greater than twofold increase in the odds of ILA when comparing the shortest quartile of telomere length to the longest quartile (OR 2.2, 95% CI 1.5-3.4, p=0.0001, and OR 2.6, 95% CI 1.4-4.9, p=0.003, respectively). In the FHS, those with ILA had shorter telomeres than those without ILA (-767 bp, 95% CI 76-1584 bp, p=0.03). Although decreased MTL was associated with chronic obstructive pulmonary disease (OR 1.3, 95% CI 1.1-1.6, p=0.01) in COPDGene, the effect estimate was less than that noted with ILA. There was no consistent association between MTL and risk of death when comparing the shortest quartile of telomere length in COPDGene and AGES-Reykjavik (HR 0.82, 95% CI 0.4-1.7, p=0.6, and HR 1.2, 95% CI 0.6-2.2, p=0.5, respectively). CONCLUSION: ILA are associated with decreased MTL.

Vascular imaging of patients with refractory Takayasu arteritis treated with tocilizumab: post hoc analysis of a randomized controlled trial.

OBJECTIVES: Tocilizumab, an anti-IL-6 receptor antibody, was investigated in patients with refractory Takayasu arteritis (TAK) in a phase 3 randomized controlled trial. In this post hoc analysis, we investigated whether tocilizumab treatment inhibited the progression of vascular lesions caused by TAK in these patients. METHODS: Included patients received at least one dose of tocilizumab and underwent CT at baseline and at week 48 after tocilizumab initiation. Three radiologists not involved in the original trial independently evaluated the CT images. Twenty-two arteries from each patient were assessed for change from baseline in wall thickness (primary endpoint), dilatation/aneurysm, stenosis/occlusion or wall enhancement for at least 96 weeks after tocilizumab initiation. Patient-level assessments were also conducted. RESULTS: In 28 patients, 86.7% of 22 arteries had improved or stable wall thickness at week 96. Proportions of patients with improved or stable, partially progressed or newly progressed lesions were 57.1%, 10.7% and 28.6%, respectively, for wall thickness; proportions with improved or stable lesions were 92.9% for dilatation/aneurysm, and 85.7% for stenosis/occlusion. Patients with newly progressed lesions, reflecting more refractory disease, were prescribed glucocorticoids at dosages that could not be reduced below 0.1 mg/kg/day at week 96. CONCLUSIONS: Approximately 60% of patients with TAK did not experience progression in wall thickness within 96 weeks after initiation of tocilizumab treatment. Few patients experienced progressed dilatation/aneurysm, or stenosis/occlusion. Wall thickness progression likely resulted from refractory TAK. Patients who experience this should be monitored regularly by imaging, and additional glucocorticoid or immunosuppressive treatment should be considered to avoid vascular progression. TRIAL REGISTRATION: Japan Pharmaceutical Information Centre number, JapicCTI-142616.

Interstitial Lung Abnormalities at CT: Subtypes, Clinical Significance, and Associations with Lung Cancer.

Interstitial lung abnormality (ILA) is defined as an interstitial change detected incidentally on CT images. It is seen in 4%-9% of smokers and 2%-7% of nonsmokers. ILA has a tendency to progress with time and is associated with respiratory symptoms, decreased exercise capability, reduced pulmonary function, and increased mortality. ILAs can be classified into three subcategories: nonsubpleural, subpleural nonfibrotic, and subpleural fibrotic. In cases of ILA, clinically significant interstitial lung disease should be identified and requires clinically driven management by a pulmonologist. Risk factors for the progression of ILA include clinical elements (ie, inhalation exposures, medication use, radiation therapy, thoracic surgery, physiologic findings, and gas exchange findings) and radiologic elements (ie, basal and peripheral predominance and fibrotic findings). It is recommended that individuals with one or more clinical or radiologic risk factors for progression of ILA be actively monitored with pulmonary function testing and CT. To avoid overcalling ILA at CT, radiologists must recognize the imaging pitfalls, including centrilobular nodularity, dependent abnormality, suboptimal inspiration, osteophyte-related lesions, apical cap and pleuroparenchymal fibroelastosis-like lesions, aspiration, and infection. There is a close association between ILA and lung cancer, and many studies have reported an increased incidence of lung cancer, worse prognoses, and/or increased pulmonary complications in relation to cancer treatment in patients with ILA. ILA is considered to be an important comorbidity in patients with lung cancer. Accordingly, all radiologists involved with body CT must have sound knowledge of ILAs owing to the high prevalence and potential clinical significance of these anomalies. An overview of ILAs, including a literature review of the associations between ILAs and lung cancer, is presented. ©RSNA, 2022.

Spindle Cell Carcinoma of the Breast: Investigation of Magnetic Resonance Imaging Features of 26 Cases.

OBJECTIVE: The aim of the study was to retrospectively investigate the fat-saturated T2-weighted sequences (FST2WI) and 3-dimensional dynamic contrast-enhanced sequence (DCE) of magnetic resonance imaging (MRI) findings of breast spindle cell carcinoma (SpCC). METHODS: Twenty-six women with surgically confirmed breast SpCC, who underwent breast MRI in 2 institutions, were enrolled in this study (mean age, 54 years; range, 27-81 years). Two breast radiologists determined the MRI findings by consensus after independent interpretations. Each MRI finding was analyzed. RESULTS: Most lesions of SpCC showed a solitary mass (92.2%). Most masses were round/oval (76.0%), had an irregular margin (88.0%), rim enhancement (72.0%), washout kinetic analysis (96.0%), hyperintensity on FST2WI (84%), hyperintensity on FST2WI and fast enhancing component on DCE (56%), and hypointense rim on FST2WI (72.0%). CONCLUSIONS: Most breast SpCC showed a solitary mass, round/oval shape, irregular margin, rim enhancement, washout kinetics, and intratumoral hyperintensity on FST2WI; a hypointense rim on FST2WI; and hyperintensity on FST2WI and fast enhancing component on DCE.

Prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer: comparison of diagnostic performances of dedicated breast PET, whole-body PET, and dynamic contrast-enhanced MRI.

PURPOSE: To compare the diagnostic performance of ring-type dedicated breast PET (dbPET), whole-body PET (WBPET), and DCE-MRI for predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). METHODS: This prospective study included 29 women with histologically proven breast cancer on needle biopsy between July 2016 and July 2019 (age: mean 55 years; range 35-78). Patients underwent WBPET followed by ring-type dbPET and DCE-MRI pre- and post-NAC for preoperative evaluation. pCR was defined as an invasive tumor that disappeared in the breast. Standardized uptake values corrected for lean body mass (SULpeak) were calculated for dbPET and WBPET scans. Maximum tumor length was measured in DCE-MRI images. Reduction rates were calculated for quantitative evaluation. Two radiologists independently evaluated the qualitative findings. Reduction rates and qualitative findings were compared between the pCR (n = 7) and non-pCR (n = 22) groups for each modality. Differences in quantitative and qualitative data between the two groups were analyzed statistically. RESULTS: Significant differences were observed in the reduction rates of dbPET and DCE-MRI (P = 0.01 and 0.03, respectively) between the two groups. Univariate and multiple logistic regression analyses revealed that SULpeak reduction rates in WBPET and dbPET (P = 0.02 and P = 0.01, respectively) and in dbPET (odds ratio, 16.00; 95% CI 1.57-162.10; P = 0.01) were significant indicators associated with pCR, respectively. No between-group differences were observed in qualitative findings in the three modalities. CONCLUSION: SULpeak reduction rate of dbPET > 82% was an independent indicator associated with pCR after NAC in breast cancer.

Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT.

OBJECTIVES: To develop a deep learning-based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists. METHODS: Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed using a convolutional neural network with Xception architecture. Of the patient data, 80% were used for training and validation and 20% were used for testing. Fivefold cross-validation was performed to evaluate the method. An average of 6688 non-contrast-enhanced CT images (slice thickness, 5 mm) were used for training. A radiologist reviewed both contrast-enhanced and non-contrast-enhanced images and identified the slices of AD. The identified slices were used as ground truth. Receiver operating characteristic curve and area under the curve (AUC) analysis was performed. Five radiologists independently evaluated the images. The accuracy, sensitivity, and specificity of the algorithm and those of the radiologists were compared. RESULTS: The AUC of the developed algorithm was 0.940, and a cutoff value of 0.400 provided accuracy of 90.0%, sensitivity of 91.8%, and specificity of 88.2%. For the radiologists, median (range) accuracy, sensitivity, and specificity were 88.8 (83.5-94.1)%, 90.6 (83.5-94.1)%, and 94.1 (72.9-97.6)%, respectively. There was no significant difference in performance in terms of accuracy, sensitivity, or specificity between the algorithm and the average performance of the radiologists (p > 0.05). CONCLUSIONS: The developed algorithm showed comparable diagnostic performance to radiologists for detecting AD, which suggests the potential of the proposed method to support clinical practice by reducing missed ADs. KEY POINTS: • A deep learning-based algorithm for detecting aortic dissection was developed using the non-contrast-enhanced CT images of 170 patients. • The algorithm had an AUC of 0.940 for detecting aortic dissection. • The accuracy, sensitivity, and specificity of the algorithm were comparable to those of radiologists.

Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network.

OBJECTIVES: To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN). METHODS: Enrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared. RESULTS: Without the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01). CONCLUSIONS: The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances. KEY POINTS: • The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity. • Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model.

Lung Adenocarcinoma at CT with 0.25-mm Section Thickness and a 2048 Matrix: High-Spatial-Resolution Imaging for Predicting Invasiveness.

Background High-spatial-resolution (HSR) CT provides detailed information and clear delineation of lung anatomy and disease states. HSR CT may have high diagnostic performance for predicting invasiveness of lung adenocarcinoma. Purpose To examine the diagnostic performance of HSR CT in predicting the invasiveness of lung adenocarcinoma. Materials and Methods In this retrospective study, 89 consecutive patients with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IVA) were included who underwent surgery for lung cancer between January 2018 and December 2019. All patients underwent HSR CT with 0.25-mm section thickness and a 2048 matrix. Two independent observers evaluated the images for the presence or absence of the following HSR CT findings: lobulation, spiculation, pleural indentation, vessel convergence, homogeneity of ground-glass opacity, reticulation, irregularity and centrality of solid portion, and air bronchiologram (irregularity, disruption, or dilatation). The total diameter (≤1.6 cm or >1.6 cm) and the longest diameter of the solid portion (≤0.8 cm or >0.8 cm) were evaluated. Logistic regression models were used to identify findings associated with MIA plus IVA. Receiver operating characteristic analysis was performed to determine diagnostic performance. Results Eighty-nine patients (mean, 69 years ± 11 [standard deviation]; 49 men) were evaluated. The size of the nodules with invasion was a mean of 2.5 cm ± 1.2. Univariable analysis revealed lobulation, spiculation, pleural indentation, irregular and central solid portion, air bronchiologram with disruption and/or irregular dilatation, and total and solid portion diameters as associated with MIA plus IVA (all, P < .05). After adjustment for age, sex, and pack-years of smoking, disruption of air bronchogram and solid portion diameter greater than 0.8 cm remained as predictors of invasiveness (P = .001 and P = .02, respectively). The diagnostic performance of these two findings combined were as follows: sensitivity of 97% (59 of 61 patients; 95% confidence interval: 94%, 100%) and specificity of 86% (19 of 22 patients; 95% confidence interval: 65%, 97%), with an area under the curve of 0.94. Conclusion Using high-spatial-resolution CT, disruption of air bronchiologram and a solid portion greater than 0.8 cm were independently associated with a greater likelihood of invasiveness in lung adenocarcinoma. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lynch and Oh in this issue.

Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT.

OBJECTIVES: This study was conducted in order to compare the effect of field of view (FOV) size on image quality between ultra-high-resolution CT (U-HRCT) and conventional high-resolution CT (HRCT). METHODS: Eleven cadaveric lungs were scanned with U-HRCT and conventional HRCT and reconstructed with five FOVs (40, 80, 160, 240, and 320 mm). Three radiologists evaluated and scored the images. Three image evaluations were performed, comparing the image quality with the five FOVs with respect to the 160-mm FOV. The first evaluation was performed on conventional HRCT images, and the second evaluation on U-HRCT images. Images were scored on normal structure, abnormal findings, and overall image quality. The third evaluation was a comparison of the images obtained with conventional HRCT and U-HRCT, with scoring performed on overall image quality. Quantitative evaluation of noise was performed by setting ROIs. RESULTS: In conventional HRCT, image quality was improved when the FOV was reduced to 160 mm. In U-HRCT, image quality, except for noise, improved when the FOV was reduced to 80 mm. In the third evaluation, overall image quality was improved in U-HRCT over conventional HRCT at all FOVs. Noise of U-HRCT increased with respect to conventional HRCT when the FOV was reduced from 160 to 40 mm. However, at 240- and 320-mm FOVs, the noise of U-HRCT and conventional HRCT showed no differences. CONCLUSIONS: In conventional HRCT, image quality did not improve when the FOV was reduced below 160 mm. However, in U-HRCT, image quality improved even when the FOV was reduced to 80 mm. KEY POINTS: • Reducing the size of the field of view to 160 mm improves diagnostic imaging quality in high-resolution CT. • In ultra-high-resolution CT, improvements in image quality can be obtained by reducing the size of the field of view to 80 mm. • Ultra-high-resolution CT produces images of higher quality compared with conventional HRCT irrespective of the size of the field of view.

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.

OBJECTIVE. The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dose CT (ULDCT). MATERIALS AND METHODS. Forty-one patients with 252 nodules were evaluated retrospectively. All patients underwent ULDCT (mean ± SD, 0.19 ± 0.01 mSv) and standard-dose CT (SDCT) (6.46 ± 2.28 mSv). ULDCT images were reconstructed using hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR), and they were postprocessed using DLD (i.e., HIR-DLD and MBIR-DLD). SDCT images were reconstructed using filtered back projection. Three independent radiologists subjectively evaluated HIR, HIR-DLD, MBIR, and MBIR-DLD images on a 5-point scale in terms of noise, streak artifact, nodule edge, clarity of small vessels, homogeneity of the normal lung parenchyma, and overall image quality. Two radiologists independently evaluated the nodules according to Lung-RADS using HIR, MBIR, HIR-DLD, and MBIR-DLD ULDCT images and SDCT images. The median scores for subjective analysis were analyzed using Wilcoxon signed rank test with Bonferroni correction. Intraobserver agreement for Lung-RADS category between ULDCT and SDCT was evaluated using the weighted kappa coefficient. RESULTS. In the subjective analysis, ULDCT with DLD showed significantly better scores than did ULDCT without DLD (p < 0.001), and MBIR-DLD showed the best scores among the ULDCT images (p < 0.001) for all items. In the Lung-RADS evaluation, HIR showed fair or moderate agreement (reader 1 and reader 2: κw = 0.46 and 0.32, respectively); MBIR, moderate or good agreement (κw = 0.68 and 0.57); HIR-DLD, moderate agreement (κw = 0.53 and 0.48); and MBIR-DLD, good agreement (κw = 0.70 and 0.72). CONCLUSION. DLD improved the image quality of both HIR and MBIR on ULDCT. MBIR-DLD was superior to HIR_DLD for image quality and for Lung-RADS evaluation.

Imaging Patterns Are Associated with Interstitial Lung Abnormality Progression and Mortality.

Rationale: Interstitial lung abnormalities (ILA) are radiologic abnormalities on chest computed tomography scans that have been associated with an early or mild form of pulmonary fibrosis. Although ILA have been associated with radiologic progression, it is not known if specific imaging patterns are associated with progression or risk of mortality. Objectives: To determine the role of imaging patterns on the risk of death and ILA progression. Methods: ILA (and imaging pattern) were assessed in 5,320 participants from the AGES-Reykjavik Study, and ILA progression was assessed in 3,167 participants. Multivariable logistic regression was used to assess factors associated with ILA progression, and Cox proportional hazards models were used to assess time to mortality. Measurements and Main Results: Over 5 years, 327 (10%) had ILA on at least one computed tomography, and 1,435 (45%) did not have ILA on either computed tomography. Of those with ILA, 238 (73%) had imaging progression, whereas 89 (27%) had stable to improved imaging; increasing age and copies of MUC5B genotype were associated with imaging progression. The definite fibrosis pattern was associated with the highest risk of progression (odds ratio, 8.4; 95% confidence interval, 2.7-25; P = 0.0003). Specific imaging patterns were also associated with an increased risk of death. After adjustment, both a probable usual interstitial pneumonia and usual interstitial pneumonia pattern were associated with an increased risk of death when compared with those indeterminate for usual interstitial pneumonia (hazard ratio, 1.7; 95% confidence interval, 1.2-2.4; P = 0.001; hazard ratio, 3.9; 95% confidence interval, 2.3-6.8;P < 0.0001), respectively. Conclusions: In those with ILA, imaging patterns can be used to help predict who is at the greatest risk of progression and early death.