[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.

An injectable fluorescent and iodinated hydrogel for preoperative localization and dual image-guided surgery of pulmonary nodules.

The widespread use of video-assisted thoracoscopic surgery (VATS) has triggered the rapid expansion in the field of computed tomography (CT)-guided preoperative localization and near-infrared (NIR) fluorescence image-guided surgery. However, its broader application has been hindered by the absence of ideal imaging contrasts that are biocompatible, minimally invasive, highly resolvable, and perfectly localized within the diseased tissue. To achieve this goal, we synthesize a dextran-based fluorescent and iodinated hydrogel, which can be injected into the tissue and imaged with both CT and NIR fluorescence modalities. By finely tuning the physical parameters such as gelation time and composition of iodinated oil (X-ray contrast agent) and indocyanine green (ICG, NIR fluorescence dye), we optimize the hydrogel for prolonged localization at the injected site without losing the dual-imaging capability. We validate the effectiveness of the developed injectable dual-imaging platform by performing image-guided resection of pulmonary nodules on tumor-bearing rabbits, which are preoperatively localized with the hydrogel. The injectable dual-imaging marker, therefore, can emerge as a powerful tool for surgical guidance.

2024 Consensus Statement on Coronary Stenosis and Plaque Evaluation in CT Angiography From the Asian Society of Cardiovascular Imaging-Practical Tutorial (ASCI-PT).

The Asian Society of Cardiovascular Imaging-Practical Tutorial (ASCI-PT) is an instructional initiative of the ASCI School designed to enhance educational standards. In 2021, the ASCI-PT was convened with the goal of formulating a consensus statement on the assessment of coronary stenosis and coronary plaque using coronary CT angiography (CCTA). Nineteen experts from four countries conducted thorough reviews of current guidelines and deliberated on eight key issues to refine the process and improve the clarity of reporting CCTA findings. The experts engaged in both online and on-site sessions to establish a unified agreement. This document presents a summary of the ASCI-PT 2021 deliberations and offers a comprehensive consensus statement on the evaluation of coronary stenosis and coronary plaque in CCTA.

Is a Camera-Type Portable X-Ray Device Clinically Feasible in Chest Imaging?: Image Quality Comparison with Chest Radiographs Taken with Traditional Mobile Digital X-Ray Devices.

Purpose: To evaluate whether the image quality of chest radiographs obtained using a camera-type portable X-ray device is appropriate for clinical practice by comparing them with traditional mobile digital X-ray devices. Materials and Methods: Eighty-six patients who visited our emergency department and underwent endotracheal intubation, central venous catheterization, or nasogastric tube insertion were included in the study. Two radiologists scored images captured with traditional mobile devices before insertion and those captured with camera-type devices after insertion. Identification of the inserted instruments was evaluated on a 5-point scale, and the overall image quality was evaluated on a total of 20 points scale. Results: The identification score of the instruments was 4.67 ± 0.71. The overall image quality score was 19.70 ± 0.72 and 15.02 ± 3.31 (p < 0.001) for the mobile and camera-type devices, respectively. The scores of the camera-type device were significantly lower than those of the mobile device in terms of the detailed items of respiratory motion artifacts, trachea and bronchus, pulmonary vessels, posterior cardiac blood vessels, thoracic intervertebral disc space, subdiaphragmatic vessels, and diaphragm (p = 0.013 for the item of diaphragm, p < 0.001 for the other detailed items). Conclusion: Although caution is required for general diagnostic purposes as image quality degrades, a camera-type device can be used to evaluate the inserted instruments in chest radiographs.

Feasibility of UTE-MRI-based radiomics model for prediction of histopathologic subtype of lung adenocarcinoma: in comparison with CT-based radiomics model.

OBJECTIVES: To assess the feasibility of the UTE-MRI radiomic model in predicting the micropapillary and/or solid (MP/S) patterns of surgically resected lung adenocarcinoma. MATERIALS AND METHODS: We prospectively enrolled 74 lesions from 71 patients who underwent UTE-MRI and CT before curative surgery for early lung adenocarcinoma. For conventional radiologic analysis, we analyzed the longest lesion diameter and lesion characteristics at both UTE-MRI and CT. Radiomic features were extracted from the volume of interest of the lesions and Rad-scores were generated using the least absolute shrinkage and selection operator with fivefold cross-validation. Six models were constructed by combining the conventional radiologic model, UTE-MRI Rad-score, and CT Rad-score. The areas under the curves (AUCs) of each model were compared using the DeLong method. Early recurrence after curative surgery was analyzed, and Kaplan-Meier survival analysis was performed. RESULTS: Twenty-four lesions were MP/S-positive, and 50 were MP/S-negative. The longitudinal size showed a small systematic difference between UTE-MRI and CT, with fair intermodality agreement of lesion characteristic (kappa = 0.535). The Rad-scores of the UTE-MRI and CT demonstrated AUCs of 0.84 and 0.841, respectively (p = 0.98). Among the six models, mixed conventional, UTE-MRI, and CT Rad-score model showed the highest diagnostic performance (AUC = 0.879). In the survival analysis, the high- and low-risk groups were successfully divided by the Rad-score in UTE-MRI (p = 0.01) and CT (p < 0.01). CONCLUSION: UTE-MRI radiomic model predicting MP/S positivity is feasible compared with the CT radiomic model. Also, it was associated with early recurrence in the survival analysis. CLINICAL RELEVANCE STATEMENT: A radiomic model utilizing UTE-MRI, which does not present a radiation hazard, was able to successfully predict the histopathologic subtype of lung adenocarcinoma, and it was associated with the patient's recurrence-free survival. KEY POINTS: • No studies have reported the ultrashort echo time (UTE)-MRI-based radiomic model for lung adenocarcinoma. • The UTE-MRI Rad-score showed comparable diagnostic performance with CT Rad-score for predicting micropapillary and/or solid histopathologic pattern. • UTE-MRI is feasible not only for conventional radiologic analysis, but also for radiomics analysis.

Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation.

The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing parenchymal diseases, including asthma and chronic obstructive pulmonary disease. Pectoralis muscle segmentation is a method for measuring muscle volume and mass for various applications. The segmentation method is based on deep-learning techniques that combine a muscle area detection model and a segmentation model. The training dataset for the detection model comprised multichannel images of patients, whereas the segmentation model was trained on 7,796 cases of the computed tomography (CT) image dataset of 1,841 patients. The dataset was expanded incrementally through an active learning process. The performance of the model was evaluated by comparing the segmentation results with manual annotations by radiologists and the volumetric differences between the CT image datasets of the same patients. The results indicated that the machine learning model is promising in segmenting the pectoralis major muscle, with good agreement between the automatic segmentation and manual annotations by radiologists. The training accuracy and loss values of the validation set were 0.9954 and 0.0725, respectively, and for segmentation, the loss value was 0.0579. This study shows the potential clinical usefulness of the machine learning model for pectoralis major muscle segmentation as a quantitative biomarker for various parenchymal and muscular diseases.

Skin Test-Guided Strategy to Select Alternative Iodinated Contrast Media in Patients With Immediate Hypersensitivity Reaction: A Prospective Confirmative Study.

BACKGROUND: Iodinated contrast media (ICM) are a common cause of drug-induced immediate hypersensitivity reaction (IHR). Repeated use of ICM is often necessary; therefore, a standardized protocol to prevent recurrence of IHR is required. OBJECTIVE: We aimed to propose an intradermal skin test (IDT)-guided strategy for previous reactors to prevent recurrence of IHR. METHODS: We conducted a prospective multicenter study from May 2018 to December 2020 and recruited patients who had experienced IHR to ICM. Once enrolled, the participants underwent IDT with a causative ICM. The alternatives for reexposure were selected using the following protocol: (1) if the IDT with the culprit ICM was positive, further skin tests with other available ICM were conducted to choose IDT-negative agents as alternatives, and (2) if the IDT with the culprit ICM was negative, a randomly changed ICM was used without additional skin tests. The recurrence and severity of hypersensitivity were assessed in subsequent computed tomography examinations. Premedication was administered according to the severity of the index event in all cases. RESULTS: A total of 496 participants were enrolled, and 299 were reexposed to ICM. Among 269 participants who followed the protocol, 228 (84.8%) completed computed tomography examinations without adverse reactions, and IHR recurred in 16 of 30 participants (53.3%) who did not follow the protocol (P < .001). In addition, application of the protocol reduced the severity of IHR in recurred cases (P = 0.003). CONCLUSIONS: Our IDT-guided strategy not only reduced recurrence of IHR to ICM but also mitigated the severity in recurred cases. This provides evidence for recommending an IDT to diagnose ICM allergy and find safe alternatives.

Role of Dual-Acquisition Noninvasive Cardiac CT Imaging for the Detection of Vasospastic Angina.

BACKGROUND: Vasospastic angina (VSA) is characterized by chest pain at rest with transient ischemic electrocardiographic changes in the ST segment, and a prompt response to nitrates. Vasospastic angina is among the most frequent of the coronary artery diseases in Asia, and coronary computed tomography angiography (CCTA) may become available as a non-invasive diagnosis method. METHODS: We prospectively enrolled 100 patients with suspected vasospastic angina at two centers from 2018 to 2020. All patients underwent baseline CCTA without a vasodilator in the early morning followed by catheterized coronary angiography and spasm testing. CCTA with intravenous infusion of nitrate (IV) was repeated within 2 weeks of baseline CCTA. Vasospastic angina as detected by CCTA was defined as significant stenosis (≥50%) with negative remodeling without definite plaques or diffuse small diameter (<2 mm) of a major coronary artery with a beaded appearance on baseline CT that completely dilated on IV nitrate CT. We analyzed diagnostic performance of dual-acquisition CCTA for the detection of vasospastic angina. RESULTS: The patients were categorized into three groups according to their provocation test result (negative, n = 36; probable positive, n = 18; positive, n = 31). The diagnostic accuracy in terms of CCTA per patient had a sensitivity of 55% (95% CI, 40-69), specificity of 89% (95% CI, 74-97), positive predictive value (PPV) of 87% (95% CI, 72-95), and negative predictive value (NPV) of 59% (95% CI, 51-67). CONCLUSIONS: Dual-acquisition CCTA can support the non-invasive detection of vasospastic angina with relatively good specificity and PPV. CCTA was helpful for non-invasive screening of variant angina.

Two Years of Experience and Methodology of Korean COVID-19 Living Clinical Practice Guideline Development.

BACKGROUND: In Korea, during the early phase of the coronavirus disease 2019 (COVID-19) pandemic, we responded to the uncertainty of treatments under various conditions, consistently playing catch up with the speed of evidence updates. Therefore, there was high demand for national-level evidence-based clinical practice guidelines for clinicians in a timely manner. We developed evidence-based and updated living recommendations for clinicians through a transparent development process and multidisciplinary expert collaboration. METHODS: The National Evidence-based Healthcare Collaborating Agency (NECA) and the Korean Academy of Medical Sciences (KAMS) collaborated to develop trustworthy Korean living guidelines. The NECA-supported methodological sections and 8 professional medical societies of the KAMS worked with clinical experts, and 31 clinicians were involved annually. We developed a total of 35 clinical questions, including medications, respiratory/critical care, pediatric care, emergency care, diagnostic tests, and radiological examinations. RESULTS: An evidence-based search for treatments began in March 2021 and monthly updates were performed. It was expanded to other areas, and the search interval was organized by a steering committee owing to priority changes. Evidence synthesis and recommendation review was performed by researchers, and living recommendations were updated within 3-4 months. CONCLUSION: We provided timely recommendations on living schemes and disseminated them to the public, policymakers and various stakeholders using webpages and social media. Although the output was successful, there were some limitations. The rigor of development issues, urgent timelines for public dissemination, education for new developers, and spread of several new COVID-19 variants have worked as barriers. Therefore, we must prepare systematic processes and funding for future pandemics.

A Modified Length-Based Grading Method for Assessing Coronary Artery Calcium Severity on Non-Electrocardiogram-Gated Chest Computed Tomography: A Multiple-Observer Study.

OBJECTIVE: To validate a simplified ordinal scoring method, referred to as modified length-based grading, for assessing coronary artery calcium (CAC) severity on non-electrocardiogram (ECG)-gated chest computed tomography (CT). MATERIALS AND METHODS: This retrospective study enrolled 120 patients (mean age ± standard deviation [SD], 63.1 ± 14.5 years; male, 64) who underwent both non-ECG-gated chest CT and ECG-gated cardiac CT between January 2011 and December 2021. Six radiologists independently assessed CAC severity on chest CT using two scoring methods (visual assessment and modified length-based grading) and categorized the results as none, mild, moderate, or severe. The CAC category on cardiac CT assessed using the Agatston score was used as the reference standard. Agreement among the six observers for CAC category classification was assessed using Fleiss kappa statistics. Agreement between CAC categories on chest CT obtained using either method and the Agatston score categories on cardiac CT was assessed using Cohen's kappa. The time taken to evaluate CAC grading was compared between the observers and two grading methods. RESULTS: For differentiation of the four CAC categories, interobserver agreement was moderate for visual assessment (Fleiss kappa, 0.553 [95% confidence interval {CI}: 0.496-0.610]) and good for modified length-based grading (Fleiss kappa, 0.695 [95% CI: 0.636-0.754]). The modified length-based grading demonstrated better agreement with the reference standard categorization with cardiac CT than visual assessment (Cohen's kappa, 0.565 [95% CI: 0.511-0.619 for visual assessment vs. 0.695 [95% CI: 0.638-0.752] for modified length-based grading). The overall time for evaluating CAC grading was slightly shorter in visual assessment (mean ± SD, 41.8 ± 38.9 s) than in modified length-based grading (43.5 ± 33.2 s) (P < 0.001). CONCLUSION: The modified length-based grading worked well for evaluating CAC on non-ECG-gated chest CT with better interobserver agreement and agreement with cardiac CT than visual assessment.

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.

[Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19]. / 한국형 COVID-19 흉부영상 진단 시행 가이드라인.

To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic unhospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.

[Korean Clinical Practice Guidelines for Adverse Reactions to Intravenous Iodinate and MRI-Gadolinium Contrast Agents: Revised Clinical Consensus and Recommendations (3rd Edition, 2022)]. / 주사용 요오드화 ì¡°ì˜ì œ 및 MRI용 가돌리늄 ì¡°ì˜ì œ ìœ í•´ë°˜ì‘ì— 대한 한국 임상진료지침: ê°œì •ëœ ìž„ìƒì  합의 및 ê¶Œê³ ì•ˆ(2022ë „ ì œ3판).

The Korean Society of Radiology and Medical Guidelines Committee amended the existing 2016 guidelines to publish the "Korean Clinical Practice Guidelines for Adverse Reactions to Iodide Contrast for Injection and Gadolinium Contrast for MRI: The Revised Clinical Consensus and Recommendations (2022 Third Edition)." Expert members recommended and approved by the Korean Society of Radiology, the Korean Academy of Asthma, Allergy and Clinical Immunology, and the Korean Nephrology Society participated together. According to the expert consensus or systematic literature review, the description of the autoinjector and connection line for the infection control while using contrast medium, the acute adverse reaction, and renal toxicity to iodized contrast medium were modified and added. We would like to introduce the revised contents.

Assessment of coronary spasms with transluminal attenuation gradient in coronary computed tomography angiography.

PURPOSE: To evaluate the imaging features of coronary spasm, including transluminal attenuation gradient (TAG) on coronary computed tomography angiography (CCTA), in patients with vasospastic angina (VA). METHODS: A total of 43 patients with a high clinical likelihood of VA were included in the study. All the subjects underwent double CCTA acquisition: CCTA without a vasodilator ('baseline CT') and CCTA during continuous intravenous nitrate infusion ('IV nitrate CT'). A catheterized ergonovine provocation test was used to determine true VA patients. Coronary spasm is classified into focal- and diffuse-types according to morphological differences. We measured TAG and contrast enhancement of the proximal ostium (ProxHU) of each coronary artery for both the baseline and IV nitrate CT. RESULTS: Twenty-four patients (55.8%) showed positive results of coronary vasospasm on the provocation test. Thirty-eight vessels showed coronary spasms (29.5%): Focal-type in nine vessels (24%), and diffuse-type in 29 (76%). In the baseline CT, LCX showed significantly lower (steeper) TAG in spasm(+) vessels than in spasm(-) vessels, while LAD and RCA showed no significant differences in TAG. The ProxHU of LAD showed significantly lower values in spasm(+) vessels than in spasm(-) vessels, while the other vessels did not show significant differences in ProxHU. For IV nitrate CT, there were no significant differences in either the TAG and ProxHU between spasm(+) and (-) vessels for all the three vessel types. In subgroup analysis for spasm(+) vessels, diffuse spasms showed significantly lower TAG than focal spasms, while the ProxHU did not differ between the two types of spasm. CONCLUSIONS: A relatively large percentage of coronary spasms present as diffuse type, and the TAG values significantly differed according to the morphological type of the coronary spasm.

A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography.

PURPOSE: We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition. MATERIALS AND METHODS: The Korean Society of Imaging Informatics in Medicine (KSIIM) organized a challenge for emphysema quantification between November 24, 2020 and January 26, 2021. Seven invited research teams participated in this challenge. In total, 558 pairs of computed tomography (CT) scans (468 pairs for the training set, and 90 pairs for the test set) from 9 hospitals were collected retrospectively or prospectively. CT acquisition followed the hospitals' protocols to reflect the real-world clinical setting. Using the training set, each team developed an algorithm that generated converted LDCT by changing the pixel values of LDCT to simulate those of standard-dose CT (SDCT). The agreement between SDCT and LDCT was evaluated using the intraclass correlation coefficient (ICC; 2-way random effects, absolute agreement, and single rater) for the percentage of low-attenuated area below -950 HU (LAA-950 HU), κ value for emphysema categorization (LAA-950 HU, <5%, 5% to 10%, and ≥10%) and cosine similarity of LAA-950 HU. RESULTS: The mean LAA-950 HU of the test set was 14.2%±10.5% for SDCT, 25.4%±10.2% for unconverted LDCT, and 12.9%±10.4%, 11.7%±10.8%, and 12.4%±10.5% for converted LDCT (top 3 teams). The agreement between the SDCT and converted LDCT of the first-place team was 0.94 (95% confidence interval: 0.90, 0.97) for ICC, 0.71 (95% confidence interval: 0.58, 0.84) for categorical agreement, and 0.97 (interquartile range: 0.94 to 0.99) for cosine similarity. CONCLUSIONS: Emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies.

Accuracy of two deep learning-based reconstruction methods compared with an adaptive statistical iterative reconstruction method for solid and ground-glass nodule volumetry on low-dose and ultra-low-dose chest computed tomography: A phantom study.

No published studies have evaluated the accuracy of volumetric measurement of solid nodules and ground-glass nodules on low-dose or ultra-low-dose chest computed tomography, reconstructed using deep learning-based algorithms. This is an important issue in lung cancer screening. Our study aimed to investigate the accuracy of semiautomatic volume measurement of solid nodules and ground-glass nodules, using two deep learning-based image reconstruction algorithms (Truefidelity and ClariCT.AI), compared with iterative reconstruction (ASiR-V) in low-dose and ultra-low-dose settings. We performed computed tomography scans of solid nodules and ground-glass nodules of different diameters placed in a phantom at four radiation doses (120 kVp/220 mA, 120 kVp/90 mA, 120 kVp/40 mA, and 80 kVp/40 mA). Each scan was reconstructed using Truefidelity, ClariCT.AI, and ASiR-V. The solid nodule and ground-glass nodule volumes were measured semiautomatically. The gold-standard volumes could be calculated using the diameter since all nodule phantoms are perfectly spherical. Subsequently, absolute percentage measurement errors of the measured volumes were calculated. Image noise was also calculated. Across all nodules at all dose settings, the absolute percentage measurement errors of Truefidelity and ClariCT.AI were less than 11%; they were significantly lower with Truefidelity or ClariCT.AI than with ASiR-V (all P<0.05). The absolute percentage measurement errors for the smallest solid nodule (3 mm) reconstructed by Truefidelity or ClariCT.AI at all dose settings were significantly lower than those of this nodule reconstructed by ASiR-V (all P<0.05). Furthermore, the lowest absolute percentage measurement errors for ground-glass nodules were observed with Truefidelity or ClariCT.AI at all dose settings. The absolute percentage measurement errors for ground-glass nodules reconstructed with Truefidelity at ultra-low-dose settings were significantly lower than those of all sizes of ground-glass nodules reconstructed with ASiR-V (all P<0.05). Image noise was lowest with Truefidelity (all P<0.05). In conclusion, the deep learning-based algorithms were more accurate for volume measurements of both solid nodules and ground-glass nodules than ASiR-V at both low-dose and ultra-low-dose settings.

Clinical efficacy of adjuvant chemotherapy in stage IB (< 4 cm) non-small cell lung cancer patients with high-risk factors.

BACKGROUND/AIMS: Adjuvant chemotherapy is the standard of care for resected stage II-IIIA non-small cell lung cancer (NCSLC). The efficacy of adjuvant chemotherapy in stage IB (< 4 cm) NSCLC with high-risk factors is controversial. METHODS: This retrospective multicenter study included 285 stage IB NSCLC patients with high-risk factors according to the 8th edition tumor, node, metastasis (TNM) classification from four academic hospitals. High-risk factors included visceral pleural invasion, vascular invasion, lymphatic invasion, lung neuroendocrine tumors, and micropapillary histology patterns. RESULTS: Of the 285 patients, 127 (44.6%) were included in the adjuvant chemotherapy group and 158 (55.4%) were included in the non-adjuvant chemotherapy group. The median follow-up was 41.5 months. Patients in the adjuvant chemotherapy group had a significantly reduced recurrence rate and risk of mortality than those in the non-adjuvant chemotherapy group (hazards ratio, 0.408; 95% confidence interval, 0.221 to 0.754; p = 0.004 and hazards ratio, 0.176; 95% confidence interval, 0.057 to 0.546; p = 0.003, respectively). Adjuvant chemotherapy should be particularly considered for the high-risk factors such as visceral pleural involvement or vascular invasion. Based on the subgroup analysis, adjuvant chemotherapy should be considered when visceral pleural involvement is present, even if the tumor size is < 3 cm. CONCLUSION: Adjuvant chemotherapy may be useful for patients with stage IB NSCLC with high-risk factors and is more relevant for patients with visceral pleural involvement or vascular invasion.