[Visualization of Borderline Coronary Artery Lesions by CT Angiography and Coronary Artery Disease Reporting and Data System]. / 관상동맥 질환 íŒë ê³¼ 자료 체계와 CT í˜ˆê´€ì¡°ì˜ìˆ ì—ì„œì˜ 경계성 관상동맥 병변.

Coronary artery disease (CAD) narrows vessel lumens at the sites of atherosclerosis, increasing the risk of myocardial ischemia or infarction. Early and accurate diagnosis of CAD is crucial to significantly improve prognosis and management. CT angiography (CTA) is a noninvasive imaging technique that enables assessment of vascular structure and stenosis with high resolution and contrast. Coronary CTA is useful in the diagnosis of CAD. Recently, the CAD-reporting and data system (CAD-RADS), a diagnostic classification system based on coronary CTA, has been developed to improve intervention efficacy in patients suspected of CAD. While the CAD-RAD is based on CTA, it includes borderline categories where interpreting the coronary artery status solely based on CTA findings may be challenging. This review introduces CTA findings that fall within the CAD-RADS categories that necessitate additional tests to decide to perform invasive coronary angiography and discusses appropriate management strategies.

Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography.

BACKGROUND: To propose a deep learning architecture for automatically detecting the complex structure of the aortic annulus plane using cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). METHODS: This study retrospectively reviewed consecutive patients who underwent TAVR between January 2017 and July 2020 at a tertiary medical center. Annulus Detection Permuted AdaIN network (ADPANet) based on a three-dimensional (3D) U-net architecture was developed to detect and localize the aortic annulus plane using cardiac CT. Patients (N = 72) who underwent TAVR between January 2017 and July 2020 at a tertiary medical center were enrolled. Ground truth using a limited dataset was delineated manually by three cardiac radiologists. Training, tuning, and testing sets (70:10:20) were used to build the deep learning model. The performance of ADPANet for detecting the aortic annulus plane was analyzed using the root mean square error (RMSE) and dice similarity coefficient (DSC). RESULTS: In this study, the total dataset consisted of 72 selected scans from patients who underwent TAVR. The RMSE and DSC values for the aortic annulus plane using ADPANet were 55.078 ± 35.794 and 0.496 ± 0.217, respectively. CONCLUSION: Our deep learning framework was feasible to detect the 3D complex structure of the aortic annulus plane using cardiac CT for TAVR. The performance of our algorithms was higher than other convolutional neural networks.

Electronic Cigarette or Vaping-Associated Lung Injury Manifested as Acute Eosinophilic Pneumonia: A Case Report.

Electronic cigarette or vaping-associated lung injury (EVALI) is a disease defined by lung injuries caused by e-cigarette use. It predominantly manifests in forms of organized pneumonia or diffuse alveolar damage but rarely as acute eosinophilic pneumonia (AEP). This report describes a 34-year-old male with acute respiratory symptoms and a vaping history of only nicotine. Chest CT revealed peripheral distributing multiple patchy consolidations and ground-glass opacities dominant in both lower lobes, bilateral diffuse interlobular septal thickening, and bilateral pleural effusion without cardiomegaly. Bronchoalveolar lavage fluids showed increased eosinophilia levels, while infectious laboratory results were all negative, enabling the diagnosis of both AEP and EVALI. Herein, we report a rare case of only-nicotine vaping EVALI manifested as AEP.

Multicentre external validation of a commercial artificial intelligence software to analyse chest radiographs in health screening environments with low disease prevalence.

OBJECTIVES: To externally validate the performance of a commercial AI software program for interpreting CXRs in a large, consecutive, real-world cohort from primary healthcare centres. METHODS: A total of 3047 CXRs were collected from two primary healthcare centres, characterised by low disease prevalence, between January and December 2018. All CXRs were labelled as normal or abnormal according to CT findings. Four radiology residents read all CXRs twice with and without AI assistance. The performances of the AI and readers with and without AI assistance were measured in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. RESULTS: The prevalence of clinically significant lesions was 2.2% (68 of 3047). The AUROC, sensitivity, and specificity of the AI were 0.648 (95% confidence interval [CI] 0.630-0.665), 35.3% (CI, 24.7-47.8), and 94.2% (CI, 93.3-95.0), respectively. AI detected 12 of 41 pneumonia, 3 of 5 tuberculosis, and 9 of 22 tumours. AI-undetected lesions tended to be smaller than true-positive lesions. The readers' AUROCs ranged from 0.534-0.676 without AI and 0.571-0.688 with AI (all p values < 0.05). For all readers, the mean reading time was 2.96-10.27 s longer with AI assistance (all p values < 0.05). CONCLUSIONS: The performance of commercial AI in these high-volume, low-prevalence settings was poorer than expected, although it modestly boosted the performance of less-experienced readers. The technical prowess of AI demonstrated in experimental settings and approved by regulatory bodies may not directly translate to real-world practice, especially where the demand for AI assistance is highest. KEY POINTS: • This study shows the limited applicability of commercial AI software for detecting abnormalities in CXRs in a health screening population. • When using AI software in a specific clinical setting that differs from the training setting, it is necessary to adjust the threshold or perform additional training with such data that reflects this environment well. • Prospective test accuracy studies, randomised controlled trials, or cohort studies are needed to examine AI software to be implemented in real clinical practice.

Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness.

OBJECTIVES: To evaluate commercial deep learning-based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). METHODS: This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. RESULTS: CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, -244.0, 283.9] vs. 45.26 [-248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score ≥ 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). CONCLUSIONS: Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. KEY POINTS: • Total Agatston scores and all vessels of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mm than with LDCT2.5-mm. • This automated software yielded a lower degree of underestimation compared with methods described in previous studies, and the degree of underestimation was lower with LDCT1.0-mm than with LDCT2.5-mm.

Identifying fragile calcifications of the aortic valve in transcatheter aortic valve replacement: iodine concentration of aortic valvular calcification by spectral CT.

OBJECTIVE: To demonstrate the relationship between spectral computed tomography (CT) measured iodine concentration and strength of aortic valvular calcification (AVC) in patients with aortic valve stenosis (AVS). METHODS: A retrospective study was performed on patients who underwent transcatheter aortic valve replacement (TAVR) for symptomatic AVS and underwent both pre and postprocedural electrocardiogram gated CT scans using a spectral CT system. Preprocedural CT was used to evaluate the volume and iodine concentration (IC) in the AVC. Postprocedural CT data were used to calculate the volume reduction percentage (VRP) of AVC. Multiple linear regression analysis was used to identify the independent variables related to the VRP in AVCs. RESULTS: A total of 94 AVCs were selected from 22 patients. The mean volume and IC of the AVCs before TAVR were 0.37 mL ± 0.15 mL and 7 mg/mL ± 10.5 mg/mL, respectively. After TAVR, a median VRP of all 94 AVCs was 18.5%. Multiple linear regression analysis showed that the IC was independently associated with the VRP (coefficient = 1.64, p < 0.001). When an optimal IC cutoff point was set at 4 mg/mL in the assessment of a fragile AVC which showed the VRP was > 18.5%, the sensitivity was 63%; specificity, 91%; positive predictive value, 88%; and negative predictive value, 71%. CONCLUSIONS: When using spectral CT to prepare the TAVR, measuring the IC of the AVC may be useful to assess the probability of AVC deformity after TAVR. KEY POINTS: • A dual-layer detector-based spectral CT enables quantifying iodine of contrast media in the aortic valve calcification (AVC) on contrast-enhanced CT images. • The AVC including iodine of contrast media on contrast-enhanced CT image may have loose compositions, associated with the deformity of AVC after TAVR. • Measuring the iodine concentration in AVC may have the potential to assess the probability of AVC deformity, which may be associated with the outcome and complications after TAVR.2.

Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging.

BACKGROUND: To propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI). METHODS: An active learning framework was developed to segment the left atrium in cardiac LGE-CMRI. Patients (n = 98) with atrial fibrillation from the Korea University Anam Hospital were enrolled. First, 20 cases were delineated for ground truths by two experts and used for training a draft model. Second, the 20 cases from the first step and 50 new cases, corrected in a human-in-the-loop manner after predicting using the draft model, were used to train the next model; all 98 cases (70 cases from the second step and 28 new cases) were trained. An additional 20 LGE-CMRI were evaluated in each step. RESULTS: The Dice coefficients for the three steps were 0.85 ± 0.06, 0.89 ± 0.02, and 0.90 ± 0.02, respectively. The biases (95% confidence interval) in the Bland-Altman plots of each step were 6.36% (-14.90-27.61), 6.21% (-9.62-22.03), and 2.68% (-8.57-13.93). Deep active learning-based annotation times were 218 ± 31 seconds, 36.70 ± 18 seconds, and 36.56 ± 15 seconds, respectively. CONCLUSION: Deep active learning reduced annotation time and enabled efficient training on limited LGE-CMRI.

Differentiating pulmonary metastasis from benign lung nodules in thyroid cancer patients using dual-energy CT parameters.

OBJECTIVES: To explore the importance of quantitative characteristics of dual-energy CT (DECT) between pulmonary metastasis and benign lung nodules in thyroid cancer. METHODS: In this retrospective study, we identified 63 patients from our institution's database with pathologically proven thyroid cancer who underwent DECT to assess pulmonary metastasis. Among these patients, 22 had 55 pulmonary metastases, and 41 had 97 benign nodules. If nodules showed increased iodine uptake on I-131 single-photon emission computed tomography-computed tomography or increased size in follow-up CT, they were considered metastatic. We compared the clinical findings and DECT parameters of both groups and performed a receiver operating characteristic analysis to evaluate the optimal cutoff values of the DECT parameters. RESULTS: Patients with metastases were significantly older than patients with benign nodules (p = 0.048). The DECT parameters of the metastatic nodules were significantly higher than those of the benign nodules (iodine concentration [IC], 5.61 ± 2.02 mg/mL vs. 1.61 ± 0.98 mg/mL; normalized IC [NIC], 0.60 ± 0.20 vs. 0.16 ± 0.11; NIC using pulmonary artery [NICPA], 0.60 ± 0.44 vs. 0.15 ± 0.11; slope of the spectral attenuation curves [λHU], 5.18 ± 2.54 vs. 2.12 ± 1.39; and Z-effective value [Zeff], 10.0 ± 0.94 vs. 8.79 ± 0.75; all p < 0.001). In the subgroup analysis according to nodule size, all DECT parameters of the metastatic nodules in all subgroups were significantly higher than those of the benign nodules (all p < 0.05). The cutoff values for IC, NIC, λHU, NICPA, and Zeff for diagnosing metastases were 3.10, 0.29, 3.57, 0.28, and 9.34, respectively (all p < 0.001). CONCLUSIONS: DECT parameters can help to differentiate metastatic and benign lung nodules in thyroid cancer. KEY POINTS: • DECT parameters can help to differentiate metastatic and benign lung nodules in patients with thyroid cancer. • DECT parameters showed a significant difference between benign lung nodules and lung metastases, even for nodules with diameters ≥ 3 mm and < 5 mm. • Among the DECT parameters, the highest diagnostic accuracy for differentiating pulmonary metastases from benign lung nodules was achieved with the NIC and IC, followed by the NICPA and λHU, and their cutoff values were 0.29, 3.10, 0.28, and 3.57, respectively.

Deep convolution neural networks to differentiate between COVID-19 and other pulmonary abnormalities on chest radiographs: Evaluation using internal and external datasets.

We aimed to evaluate the performance of convolutional neural networks (CNNs) in the classification of coronavirus disease 2019 (COVID-19) disease using normal, pneumonia, and COVID-19 chest radiographs (CXRs). First, we collected 9194 CXRs from open datasets and 58 from the Korea University Anam Hospital (KUAH). The number of normal, pneumonia, and COVID-19 CXRs were 4580, 3884, and 730, respectively. The CXRs obtained from the open dataset were randomly assigned to the training, tuning, and test sets in a 70:10:20 ratio. For external validation, the KUAH (20 normal, 20 pneumonia, and 18 COVID-19) dataset, verified by radiologists using computed tomography, was used. Subsequently, transfer learning was conducted using DenseNet169, InceptionResNetV2, and Xception to identify COVID-19 using open datasets (internal) and the KUAH dataset (external) with histogram matching. Gradient-weighted class activation mapping was used for the visualization of abnormal patterns in CXRs. The average AUC and accuracy of the multiscale and mixed-COVID-19Net using three CNNs over five folds were (0.99 ± 0.01 and 92.94% ± 0.45%), (0.99 ± 0.01 and 93.12% ± 0.23%), and (0.99 ± 0.01 and 93.57% ± 0.29%), respectively, using the open datasets (internal). Furthermore, these values were (0.75 and 74.14%), (0.72 and 68.97%), and (0.77 and 68.97%), respectively, for the best model among the fivefold cross-validation with the KUAH dataset (external) using domain adaptation. The various state-of-the-art models trained on open datasets show satisfactory performance for clinical interpretation. Furthermore, the domain adaptation for external datasets was found to be important for detecting COVID-19 as well as other diseases.

Blood flow volume of left atrial appendage measured by magnetic resonance imaging is improved after radiofrequency catheter ablation of atrial fibrillation.

INTRODUCTION: Hemodynamics of left atrial appendage (LAA) is an important factor for future risk of ischemic stroke in atrial fibrillation (AF) patients. Velocity encoded cardiac magnetic resonance imaging (VENC-MRI) can evaluate blood flow volume of LAA without any invasive procedures. We aimed to evaluate the association between radiofrequency catheter ablation (RFCA) and LAA hemodynamics measured by MRI. METHODS AND RESULTS: Consecutive RFCA cases in a single arrhythmia center were retrospectively analyzed. A total of 3120 AF patients who underwent first RFCA were analyzed. Among these patients 360 patients had both pre- and post-RFCA VENC-MRI evaluation. Atrial fibrillation was non-paroxysmal in 174 (48.3%) patients. Mean VENC-MRI (ml/sec) was significantly improved after RFCA with 49.93 ± 32.92 and 72.00 ± 34.82 for pre- and post-RFCA, respectively. Patients with non-paroxysmal AF (∆VENC-MRI = 14.63 ± 40.67 vs. 30.03 ± 35.37; p < .001) and low pre-RFCA VENC-MRI (∆VENC-MRI = 17.19 ± 38.35 vs. 50.35 ± 29.12; p < .001) had significantly higher improvement in VENC-MRI. Those who experienced late recurrence before post-RFCA MRI had significantly less improvement in LAA flow volume (∆VENC-MRI = 15.55 ± 41.41 vs. 26.18 ± 36.77; p = .011). Late recurrence and pre-RFCA VENC-MRI were significantly associated with ∆VENC-MRI after adjusting covariates. Patients who were AF before RFCA but maintained sinus rhythm after RFCA showed greatest improvement in VENC-MRI. CONCLUSION: Effective rhythm control through RFCA can be associated with significant improvement in LAA hemodynamics. Low pre-RFCA VENC-MRI and absence of late recurrence were associated with greater improvement in LAA hemodynamics.

[Diagnosis of Hypersensitivity Pneumonitis: 2020 Clinical Practice Guideline]. / 2020ë „ ê°œì • 진료 치침에 따른 ê³¼ë¯¼ì„±íë ´ì˜ 진단.

Hypersensitivity pneumonitis (HP) is an interstitial lung disease (ILD) characterized by an inhaled inciting antigen that leads to the inflammation of the lung parenchyma and small airway with immunologic reactions. Over the last decades, the most effective therapeutic option for HP has been limited to antigen avoidance. The differential diagnosis of HP from other ILDs is the beginning of treatment as well as diagnosis. However, the presence of several overlapping clinical and radiologic features makes differentiating HP from other ILDs particularly challenging. In 2020, a multidisciplinary committee of experts from the American Thoracic Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax suggested a new clinical practice guideline classifying HP into nonfibrotic and fibrotic phenotypes on the basis of chest high-resolution CT (HRCT) findings. Therefore, we introduced a new diagnostic algorithm based on chest HRCT in the clinical practice guideline for the diagnosis of HP.

[Peer Review of Teleradiology at a Teleradiology Clinic: Comparison of Unacceptable Diagnosis and Clinically Significant Discrepancy between Radiology Sections and Imaging Modalities]. / ì›ê²©íŒë ì˜ë£Œê¸°ê´€ì—ì„œ 시행한 ì›ê²©íŒë ì˜ 동료평가: ì„¸ë¶€ì „ê³µë¶„ì•¼ì™€ 촬영장비에 따른 íŒë ë¶ˆì¼ì¹˜ ì •ë„ì™€ 불일치 íŒë ì˜ ìž„ìƒì  의의 비교.

Purpose: The purpose of this study was to evaluate the rates of unacceptable diagnosis and clinically significant diagnostic discrepancy in radiology sections and imaging modalities through a peer review of teleradiology. Materials and Methods: Teleradiology peer reviews in a Korean teleradiology clinic in 2018 and 2019 were included. The peer review scores were classified as acceptable and unacceptable diagnoses and clinically insignificant and significant diagnostic discrepancy. The diagnostic discrepancy rates and clinical significance were compared among radiology sections and imaging modalities using the chi-square test. Results: Of 1312 peer reviews, 117 (8.9%) cases had unacceptable diagnoses. Of 462 diagnostic discrepancies, the clinically significant discrepancy was observed in 104 (21.6%) cases. In radiology sections, the unacceptable diagnosis was highest in the musculoskeletal section (21.4%) (p < 0.05), followed by the abdominal section (7.3%) and neuro section (1.3%) (p < 0.05). The proportion of significant discrepancy was higher in the chest section (32.7%) than in the musculoskeletal (19.5%) and abdominal sections (17.1%) (p < 0.05). Regarding modalities, the number of unacceptable diagnoses was higher with MRI (16.2%) than plain radiology (7.8%) (p < 0.05). There was no significant difference in significant discrepancy. Conclusion: Peer review provides the rates of unacceptable diagnosis and clinically significant discrepancy in teleradiology. These rates also differ with subspecialty and modality.

Nitrates administered by spray versus tablet: comparison of coronary vasodilation on CT angiography.

OBJECTIVES: To compare the coronary vasodilation effects of spray with those induced by tablet administration in coronary CT angiography (CCTA). METHODS: A total of 2024 patients who underwent CCTA were identified for this retrospective study, including 828 patients with spray (spray group) and 1169 with tablets (tablet group). Of these, 93 patients underwent CCTA at least twice using both spray and tablets. The number of measurable segments and diameters of all 18 segments was measured. The number of measurable segments was compared between groups. RESULTS: No statistically significant differences were evident between these two groups in terms of clinical characteristics. All coronary segments except the ramus intermedius (RI) and left posterior descending artery (L-PDA) were significantly larger in the spray group than in the tablet group (all p < 0.001). In peripheral and branch vessels, as well as in central and main coronary arteries, the diameters were significantly larger in the spray group than in the tablet group (all p < 0.001). Although not always statistically significant, all coronary segments tended to be more measurable on CCTA with spray than with tablet. In the subgroup that underwent CCTA twice using both spray and tablets, all coronary segments except the RI, obtuse marginal artery 2 (OM2), and L-PDA were significantly larger in the spray group than in the tablet group (all p < 0.05). CONCLUSION: Lingual isosorbide dinitrate (ISDN) spray was more efficacious than sublingual nitroglycerin (NTG) tablets in coronary vasodilation for CCTA. Therefore, lingual ISDN spray should be preferred over sublingual NTG tablets for CCTA. KEY POINTS: • Lingual ISDN spray was more efficacious than sublingual NTG tablet for coronary vasodilation in coronary CT angiography, even in elderly patients. • The diameters of all coronary segments except RI and L-PDA were significantly larger, and there were significantly more coronary segments greater than 1.5 mm, except RI and L-PDA, in the spray group than in the tablet group in the whole study group. • Even in peripheral and branch vessels, the diameters of coronary arteries were significantly larger in the spray group than in the tablet group, and they were also larger in elderly patients.

Evaluation of Left Atrial Appendage Isolation Using Cardiac MRI after Catheter Ablation of Atrial Fibrillation: Paradox of Appendage Reservoir.

OBJECTIVE: To assess the effect of left atrial appendage (LAA) isolation on LAA emptying and left atrial (LA) function using cardiac MRI in patients who underwent successful catheter ablation of atrial fibrillation (AF). MATERIALS AND METHODS: This retrospective study included 84 patients (mean age, 59 ± 10 years; 67 males) who underwent cardiac MRI after successful catheter ablation of AF. According to the electrical activity of LAA after catheter ablation, patients showed either LAA isolation or LAA normal activity. The LAA emptying phase (LAA-EP, in the systolic phase [SP] or diastolic phase), LAA emptying flux (LAA-EF, mL/s), and LA ejection fraction (LAEF, %) were evaluated by cardiac MRI. RESULTS: Of the 84 patients, 61 (73%) and 23 (27%) patients showed LAA normal activity and LAA isolation, respectively. Incidence of LAA emptying in SP was significantly higher in LAA isolation (91% vs. 0%, p < 0.001) than in LAA normal activation. LAA-EF was significantly lower in LAA isolation (40.1 ± 16.2 mL/s vs. 80.2 ± 25.1 mL/s, p < 0.001) than in LAA normal activity. Furthermore, LAEF was significantly lower in LAA isolation (23.7% ± 11.2% vs. 31.1% ± 16.6%, p = 0.04) than in LAA normal activity. Multivariate analysis demonstrated that the LAA-EP was independent from LAEF (p = 0.01). CONCLUSION: LAA emptying in SP may be a critical characteristic of LAA isolation, and it may adversely affect the LAEF after catheter ablation of AF.

Application of Dual-Energy Spectral Computed Tomography to Thoracic Oncology Imaging.

Computed tomography (CT) is an important imaging modality in evaluating thoracic malignancies. The clinical utility of dual-energy spectral computed tomography (DESCT) has recently been realized. DESCT allows for virtual monoenergetic or monochromatic imaging, virtual non-contrast or unenhanced imaging, iodine concentration measurement, and effective atomic number (Zeff map). The application of information gained using this technique in the field of thoracic oncology is important, and therefore many studies have been conducted to explore the use of DESCT in the evaluation and management of thoracic malignancies. Here we summarize and review recent DESCT studies on clinical applications related to thoracic oncology.

Clinical implications of thoracic duct dilatation in patients with chronic liver disease.

This study aimed to investigate the association between the degree of thoracic duct dilatation and the progression of chronic liver disease.In this cross-sectional and retrospective study, 179 patients (mean age, 60.9 years; 114 men) with chronic liver disease who underwent chest CT were enrolled. Dilatation of the left distal thoracic ducts (DTD) was measured and divided into the following 3 grades according to the maximum transverse diameter: grade 0, invisible thoracic duct; grade 1, visible duct with <5-mm diameter; grade 2, diameter of ≥5 mm. Statistical analyses were conducted using the binary logistic regression model.The proportion of grade 2 DTD was notably higher as the chronic liver disease progressed to cirrhosis. Visible DTD on chest CT was significantly related to the presence of cirrhosis (odds ratio [OR], 3.809; P = .027) and significant varix (OR, 3.211; P = .025). Grade 2 DTD was observed more frequently in patients with ascites (OR, 2.788; P = .039). However, 40% of patients with cirrhosis and ascites still exhibited no visible DTD while demonstrating significant amount of ascites, and their ascites were more predominant of recent onset and transient than that observed in other patients (85.7% vs 48.4%, P = .010 and 66.7% vs 29.0%, P = .009, respectively).The degree of thoracic duct dilatation is significantly associated with progression to cirrhosis and advancement of portal hypertension. Further, insufficient lymph drainage to DTD might contribute to the development of ascites.

Relationship between Coronary Iodine Concentration Determined Using Spectral CT and the Outcome of Percutaneous Coronary Intervention in Patients with Chronic Total Occlusion.

PURPOSE: To evaluate the feasibility of coronary iodine concentration (CIC) by using spectral CT in the assessment of the outcome of percutaneous coronary intervention (PCI) for chronic total occlusion (CTO). MATERIALS AND METHODS: In total, 50 consecutive patients underwent preprocedural coronary CT angiography with spectral CT prior to their staged PCI for CTO between June 2017 and July 2018. Iodine density maps, referred to as iodine-no-water maps throughout, with spectral CT provided the CIC at proximal CTO (CTO-CIC). Depending on the outcome of PCI, all CTO lesions were divided into two groups: failed PCI and successful PCI. The receiver operating characteristic curve was used to determine the cutoff values of CTO-CIC in the assessment of the outcome of PCI for CTO. RESULTS: Of the 50 CTO lesions in 50 patients, 34 (68%) and 16 (32%) were assigned to the successful PCI and failed PCI groups, respectively. The mean CTO-CIC was significantly less in the failed PCI group than in the successful PCI group (1.3 mg/mL ± 0.9 [standard deviation] vs 5.2 mg/mL ± 2.5; P < .001). A low CTO-CIC (≤ 2.5 mg/mL) predicted failed PCI with 87% sensitivity, 79% specificity, 79% positive predictive value, and 90% negative predictive value. At multivariable analysis, the low CTO-CIC was significantly associated with the failed PCI (odds ratio, 27.0; 95% confidence interval: 4.9, 147.6; P < .0001). CONCLUSION: The CTO-CIC determined by using spectral CT may be useful in the assessment of the outcome of staged PCI for CTO.See also the commentary by Rubinshtein and Blankstein in this issue.© RSNA, 2020.

Differences in bone density on chest CT according to smoking status in males without chronic obstructive lung disease.

The goals of this study were to determine whether bone density measured using CT (CTBD) can show significant differences in bone loss according to smoking status and pack-years, and to examine the correlation between CTBD and bone mineral density when measured by dual-energy X-ray absorptiometry (DEXA-BMD) in males without chronic obstructive pulmonary disease (COPD). In this cross-sectional study, 1,011 males without airflow obstruction ≥50 years old were included. CTBD and DEXA-BMD were compared among groups with different smoking statuses. The correlation between CTBD and DEXA-BMD and the association of CTBD with pack-years were also investigated. CTBD of all vertebral bodies (VBs) and DEXA-BMD of all VBs without L1 showed significant differences among never, former, and current smokers. CTBD was significantly lowest in ≥30-pack-year smokers and was significantly lower in ≥30-pack-year smokers than in <15-pack-year smokers (all P < 0.05). There were significant correlations between DEXA-BMD and CTBD at all VB levels (correlation coefficient [r], 0.448~0.640; all P < 0.01). A lower CTBD had a significant association with a 15 ≤ x < 30-pack-year smoking history and ≥30-pack-year smoking history, while there was no association with never-smokers. In conclusion, CTBD demonstrated significant differences in bone quality according to smoking status and pack-years in males without COPD.

A Stepwise Diagnostic Approach to Cystic Lung Diseases for Radiologists.

Lung cysts are commonly seen on computed tomography (CT), and cystic lung diseases show a wide disease spectrum. Thus, correct diagnosis of cystic lung diseases is a challenge for radiologists. As the first diagnostic step, cysts should be distinguished from cavities, bullae, pneumatocele, emphysema, honeycombing, and cystic bronchiectasis. Second, cysts can be categorized as single/localized versus multiple/diffuse. Solitary/localized cysts include incidental cysts and congenital cystic diseases. Multiple/diffuse cysts can be further categorized according to the presence or absence of associated radiologic findings. Multiple/diffuse cysts without associated findings include lymphangioleiomyomatosis and Birt-Hogg-Dubé syndrome. Multiple/diffuse cysts may be associated with ground-glass opacity or small nodules. Multiple/diffuse cysts with nodules include Langerhans cell histiocytosis, cystic metastasis, and amyloidosis. Multiple/diffuse cysts with ground-glass opacity include pneumocystis pneumonia, desquamative interstitial pneumonia, and lymphocytic interstitial pneumonia. This stepwise radiologic diagnostic approach can be helpful in reaching a correct diagnosis for various cystic lung diseases.

Accuracy of Model-Based Iterative Reconstruction for CT Volumetry of Part-Solid Nodules and Solid Nodules in Comparison with Filtered Back Projection and Hybrid Iterative Reconstruction at Various Dose Settings: An Anthropomorphic Chest Phantom Study.

OBJECTIVE: To investigate the accuracy of model-based iterative reconstruction (MIR) for volume measurement of part-solid nodules (PSNs) and solid nodules (SNs) in comparison with filtered back projection (FBP) or hybrid iterative reconstruction (HIR) at various radiation dose settings. MATERIALS AND METHODS: CT scanning was performed for eight different diameters of PSNs and SNs placed in the phantom at five radiation dose levels (120 kVp/100 mAs, 120 kVp/50 mAs, 120 kVp/20 mAs, 120 kVp/10 mAs, and 80 kVp/10 mAs). Each CT scan was reconstructed using FBP, HIR, or MIR with three different image definitions (body routine level 1 [IMR-R1], body soft tissue level 1 [IMR-ST1], and sharp plus level 1 [IMR-SP1]; Philips Healthcare). The SN and PSN volumes including each solid/ground-glass opacity portion were measured semi-automatically, after which absolute percentage measurement errors (APEs) of the measured volumes were calculated. Image noise was calculated to assess the image quality. RESULTS: Across all nodules and dose settings, the APEs were significantly lower in MIR than in FBP and HIR (all p < 0.01). The APEs of the smallest inner solid portion of the PSNs (3 mm) and SNs (3 mm) were the lowest when MIR (IMR-R1 and IMR-ST1) was used for reconstruction for all radiation dose settings. (IMR-R1 and IMR-ST1 at 120 kVp/100 mAs, 1.06 ± 1.36 and 8.75 ± 3.96, p < 0.001; at 120 kVp/50 mAs, 1.95 ± 1.56 and 5.61 ± 0.85, p = 0.002; at 120 kVp/20 mAs, 2.88 ± 3.68 and 5.75 ± 1.95, p = 0.001; at 120 kVp/10 mAs, 5.57 ± 6.26 and 6.32 ± 2.91, p = 0.091; at 80 kVp/10 mAs, 5.84 ± 1.96 and 6.90 ± 3.31, p = 0.632). Image noise was significantly lower in MIR than in FBP and HIR for all radiation dose settings (120 kVp/100 mAs, 3.22 ± 0.66; 120 kVp/50 mAs, 4.19 ± 1.37; 120 kVp/20 mAs, 5.49 ± 1.16; 120 kVp/10 mAs, 6.88 ± 1.91; 80 kVp/10 mAs, 12.49 ± 6.14; all p < 0.001). CONCLUSION: MIR was the most accurate algorithm for volume measurements of both PSNs and SNs in comparison with FBP and HIR at low-dose as well as standard-dose settings. Specifically, MIR was effective in the volume measurement of the smallest PSNs and SNs.