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Background: Current guidelines consider atrial fibrillation (AF) type as the prognostic factor for a recommendation of catheter ablation. We aimed to determine whether LA and LA appendage (LAA) volumes measured using multislice computed tomography (MSCT) were related to long-term outcomes in AF following radiofrequency catheter ablation (RFCA). Methods: We evaluated 152 consecutive patients with drug-refractory AF (median age, 55.8 ± 9.6 years), including 110 male patients, who underwent RFCA in a single center. All patients underwent MSCT imaging for anatomical assessment. The endpoint of this study was documented AF recurrence after RFCA. Results: The overall procedure success rate was 77.6% (n = 118) during a mean follow-up period of 12.6 months. The LA volume was significantly larger for those who experienced AF recurrence after RFCA than for the patients without recurrent AF after the procedure (153.8 ± 29.9 mL vs. 139.2 ± 34.1 mL, p = 0.025). However, LAA volumes were nearly equivalent between the patients with and without AF recurrence after RFCA (16.2 ± 6.3 mL and 14.7 ± 6.5 mL, respectively; p = 0.235). LA volume ≥ 153.2 mL was the optimal cutoff value for estimating AF recurrence after RFCA, with 94% sensitivity and 66% specificity. LA volume remained an independent predictor of both AF recurrence and permanent AF. Conclusions: LA volume as assessed by MSCT might be helpful for identifying patients likely to achieve successful AF ablation. LA volume ≥ 153.2 mL, but not LAA volume, showed good accuracy in predicting AF recurrence after RFCA. [ABSTRACT FROM AUTHOR]

Aim: To study the role of sclerostin in periodontal ligament (PDL) as a homeostatic regulator in biophysical‐force‐induced tooth movement (BFTM). Materials and Methods: BFTM was performed in rats, followed by microarray, immunofluorescence, in situ hybridization, and real‐time polymerase chain reaction for the detection and identification of the molecules. The periodontal space was analysed via micro‐computed tomography. Effects on osteoclastogenesis and bone resorption were evaluated in the bone‐marrow‐derived cells in mice. In vitro human PDL cells were subjected to biophysical forces. Results: In the absence of BFTM, sclerostin was hardly detected in the periodontium except in the PDL and alveolar bone in the furcation region and apex of the molar roots. However, sclerostin was up‐regulated in the PDL in vivo by adaptable force, which induced typical transfiguration without changes in periodontal space as well as in vitro PDL cells under compression and tension. In contrast, the sclerostin level was unaffected by heavy force, which caused severe degeneration of the PDL and narrowed periodontal space. Sclerostin inhibited osteoclastogenesis and bone resorption, which corroborates the accelerated tooth movement by the heavy force. Conclusions: Sclerostin in PDL may be a key homeostatic molecule in the periodontium and a biological target for the therapeutic modulation of BFTM. [ABSTRACT FROM AUTHOR]

Active learning is a unique abstraction of machine learning techniques where the model/algorithm could guide users for annotation of a set of data points that would be beneficial to the model, unlike passive machine learning. The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set. Multiple frameworks for active learning combined with deep learning have been proposed, and the majority of them are dedicated to classification tasks. Herein, we explore active learning for the task of segmentation of medical imaging data sets. We investigate our proposed framework using two datasets: 1.) MRI scans of the hippocampus, 2.) CT scans of pancreas and tumors. This work presents a query-by-committee approach for active learning where a joint optimizer is used for the committee. At the same time, we propose three new strategies for active learning: 1.) increasing frequency of uncertain data to bias the training data set; 2.) Using mutual information among the input images as a regularizer for acquisition to ensure diversity in the training dataset; 3.) adaptation of Dice log-likelihood for Stein variational gradient descent (SVGD). The results indicate an improvement in terms of data reduction by achieving full accuracy while only using 22.69% and 48.85% of the available data for each dataset, respectively. [ABSTRACT FROM AUTHOR]

The patient was then diagnosed with cervical cancer post-operation, with radiation enterocolitis, intestinal obstruction, and SIIF. A computed tomography (CT) demonstrated a potential small intestinal obstruction, edema of the sigmoid colon and surrounding small intestinal wall, a potential abscess in the left iliopsoas, and retroperitoneal and pelvic exudation (Figure 5). To the Editor, Intestinal fistula (IF) is defined as an abnormal opening of the intestine that causes the leaking of intestinal contents, resulted in a series of pathophysiological issues.1 The etiology of IF can be congenital due to developmental defects, or be associated with inflammation, tumor, trauma, surgery, or radiochemotherapy-related complications.2,3 IF can be classified as high and low according to its location. Treatment experience of delayed massive gastrointestinal bleeding caused by intra-abdominal arteriointestinal fistula in gastric cancer patients after radical gastrectomy. [Extracted from the article]

Idiopathic pneumoperitoneum has an unknown etiology despite exploratory laparotomy. However, it may occur without definite abdominal symptoms; thus, adequate management could be in clinical dilemma. We experienced three cases of idiopathic nonsurgical pneumoperitoneum in healthy individuals during a health check-up. Their cases were not accompanied by any relevant etiology or definite abdominal symptoms. All of the three cases exhibited a benign clinical course. The three patients underwent an abdominal computed tomography (CT) scan as part of a health check-up program, which incidentally revealed free air in the right paracolic gutter without evidence of visceral perforation or inflammation. Among the three cases, two patients underwent colonoscopy before abdominal CT, whereas one patient did not. Two cases were completely asymptomatic and were observed without any treatment in the outpatient clinic. Only the third case with minimal symptoms was treated conservatively for a short time. If a small amount of free air typically located in the right paracolic gutter is detected in the absence of perforation during colonoscopy, close observation without unnecessary treatment would be sufficient. [ABSTRACT FROM AUTHOR]

Objectives: To validate a method for performing myocardial segmentation based on coronary anatomy using coronary CT angiography (CCTA).Methods: Coronary artery-based myocardial segmentation (CAMS) was developed for use with CCTA. To validate and compare this method with the conventional American Heart Association (AHA) classification, a single coronary occlusion model was prepared and validated using six pigs. The unstained occluded coronary territories of the specimens and corresponding arterial territories from CAMS and AHA segmentations were compared using slice-by-slice matching and 100 virtual myocardial columns.Results: CAMS more precisely predicted ischaemic area than the AHA method, as indicated by 95% versus 76% (p < 0.001) of the percentage of matched columns (defined as percentage of matched columns of segmentation method divided by number of unstained columns in the specimen). According to the subgroup analyses, CAMS demonstrated a higher percentage of matched columns than the AHA method in the left anterior descending artery (100% vs. 77%; p < 0.001) and mid- (99% vs. 83%; p = 0.046) and apical-level territories of the left ventricle (90% vs. 52%; p = 0.011).Conclusions: CAMS is a feasible method for identifying the corresponding myocardial territories of the coronary arteries using CCTA.Key Points: • CAMS is a feasible method for identifying corresponding coronary territory using CTA • CAMS is more accurate in predicting coronary territory than the AHA method • The AHA method may underestimate the ischaemic territory of LAD stenosis. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate whether plaque characteristics as assessed by coronary computed tomography angiography (CCTA) were associated with the presence of a thin-cap fibroatheroma (TCFA)-a precursor of plaque rupture-defined by optical coherence tomography (OCT) in a section-to-section-level comparison.Methods: From 28 symptomatic patients, 31 coronary lesions were evaluated on 727 cross-sections co-registered by both CCTA and OCT. CCTA plaque characteristics included low attenuation plaque (LAP, <30 HU), napkin ring sign (NRS), positive remodelling (PR, remodelling index ≥1.10), and spotty calcification and plaque area and plaque burden. By OCT, presence of TCFA, lumen area and arc of lipid were determined.Results: OCT revealed a TCFA in 69 (9.4%) sections from 19 (61.2 %) lesions. In per-section analysis, OCT-TCFA showed higher frequency of CCTA-detected LAP (58.0% vs. 18.5%), NRS (31.9% vs. 8.8%) and PR (68.1% vs. 48.0%) and greater plaque burden (70.6% vs. 61.9%) as compared to sections without OCT-TCFA (all p < 0.05). In multivariable analysis, LAP (odds ratio [OR] 4.05, p < 0.001) and NRS (OR 2.47, p = 0.005) were associated with OCT-TCFA. CCTA-measured lumen area correlated well with OCT-measured lumen area (R = 0.859, limits of agreement -0.5 ± 3.7 mm2).Conclusions: LAP and NRS in CCTA were associated with the presence of OCT-defined TCFA in a section-to-section comparison.Key Points: • CT-defined LAP and NRS were associated with OCT-defined TCFA • OCT-TCFA showed higher frequency of LAP, NRS, PR and greater plaque burden • Non-calcified plaque area was correlated with OCT-measured lipid arc. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is one of the worst cancers with bad prognosis despite systemic chemotherapy and radiotherapy after surgery.Methods: In this study, 71 patients with GBM were enrolled and randomly assigned to two groups: Receiving radiotherapy with concomitant and adjuvant temozolomide (TMZ) (TMZ, standard therapy) after surgery, or receiving radiotherapy with concomitant and adjuvant local delivery of nimustine (ACNU) rendezvousing with oral TMZ (rendezvous therapy). In the follow-up of all patients and the progression-free survival (PFS), overall survival (OS), Karnofsky performance score (KPS) and toxicities were recorded.Results: For the whole cohort, the median OS was 18.0 months, and the median PFS was 7.8 months. A significantly longer OS was observed in patients received rendezvous therapy than those who receiving standard therapy (18.5 months vs. 16.0 months; P = 0.014), as well as PFS (8.8 months vs. 7.0 months; P = 0.008). The KPS ≥70 rates were 81.8%, 40.9%, 20.5% in 1, 2, and 3 years for the rendezvous therapy group, significantly superior to standard therapy group. The most common toxicities were tolerable gastrointestinal reaction, liver dysfunction, and hematological toxicities, which were relieved with symptomatic treatment. Grade 3 or 4 toxicity was documented in 8 (18.3%) patients in rendezvous therapy group, while it was observed in 6 (22.2%) patients in standard therapy group during whole treatment process.Conclusions: Compared to standard therapy, the antitumor effects of rendezvous therapy were more effective in GBM patients without increasing the toxicities. [ABSTRACT FROM AUTHOR]

Crohn’s disease (CD) is an idiopathic inflammatory bowel disease involving the small and/or large intestine. More than 50% of Western CD patients and up to 88% of Asian CD patients may have small intestinal involvement. Video capsule endoscopy (VCE) has a higher diagnostic yield than small bowel barium radiography and computed tomography enterography for the detection of small intestinal involvement of CD. VCE also provides diagnostic yields comparable to magnetic resonance- (MR-) based enterography or enteroclysis and may have several advantages over MR-based tests for the detection of early small intestinal lesions. Several studies have suggested the use of VCE-based disease activity scoring systems to evaluate small intestinal mucosal disease activity, although their clinical relevance needs to be further studied. A possible indication for VCE is recurrence monitoring after complete surgical excision of CD-involved segments but its usefulness and efficacy compared with conventional endoscopy should be evaluated. The capsule retention rate ranges from 0 to 5.4% in suspected CD patients and from 0 to 13.2% in established CD patients. If VCE is necessary, significant small bowel stricture should be ruled out before VCE by performing a patency capsule study and/or small bowel radiological study in suspected or established CD patients. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to evaluate the role of preoperative cardiac computed tomography (CT) for adults with congenital cardiac septal defect (CSD). Methods: Sixty-five consecutive patients who underwent preoperative CT and surgery for CSD were included. The diagnostic accuracy of CT and the concordance rate of the subtype classification of CSD were evaluated using surgical findings as the reference standard. Sixty-five patients without CSD who underwent cardiac valve surgery were used as a control group. An incremental value of CT over echocardiography was described retrospectively. Results: Sensitivity and specificity of CT for diagnosis of CSD were 95 % and 100 %, respectively. The concordance rate of subtype classification was 91 % in CT and 92 % in echocardiography. The maximum size of the defect measured by CT correlated well with surgical measurement ( r = 0.82), and the limit of agreement was -0.9 ± 7.42 mm. In comparison with echocardiography, CT was able to detect combined abnormalities in three cases, and exclusively provided correct subtype classification or clarified suspected abnormal findings found on echocardiography in seven cases. Conclusions: Cardiac CT can accurately demonstrates CSD in preoperative adult patients. CT may have an incremental role in preoperative planning, particularly in those with more complex anatomy. Key Points: • Cardiac CT can demonstrate cardiac septal defect accurately in preoperative planning. • Cardiac CT can demonstrate combined abnormalities of cardiac septal defect. • Cardiac CT may have an incremental role over echocardiography in complex anatomy. [ABSTRACT FROM AUTHOR]

Background: Treatment efficacy may differ among patients with nasopharyngeal carcinoma (NPC) at similar tumor–node–metastasis stages. Moreover, end-of-treatment tumor regression is a reliable indicator of treatment sensitivity. This study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters could predict sensitivity to neck–lymph node radiotherapy in patients with NPC. Methods: Overall, 388 lymph nodes were collected from 98 patients with NPC who underwent pretreatment DECT. The patients were divided into complete response (CR) and partial response (PR) groups. Clinical characteristics and quantitative DECT parameters were compared between the groups, and the optimal predictive ability of each parameter was determined using receiver operating characteristic (ROC) analysis. A nomogram prediction model was constructed and validated using univariate and binary logistic regression. Results: DECT parameters were higher in the CR group than in the PR group. The iodine concentration (IC), normalized IC, Mix-0.6, spectral Hounsfield unit curve slope, effective atomic number, and virtual monoenergetic images were significantly different between the groups. The area under the ROC curve of the DECT parameters was 0.73–0.77. Based on the binary logistic regression, a column chart was constructed using 10 predictive factors, including age, sex, N stage, maximum lymph node diameter, arterial phase NIC, venous phase NIC, λHU and spectral Hounsfield units at 70 keV. The area under the ROC curve value of the constructed model was 0.813, with a sensitivity and specificity of 85.6% and 81.3%, respectively. Conclusion: Quantitative DECT parameters could effectively predict the sensitivity of NPC to radiotherapy. Therefore, DECT parameters and NPC clinical features can be combined to construct a nomogram with high predictive power and used as a clinical analytical tool. [ABSTRACT FROM AUTHOR]

As a minimally invasive surgery to treat atrial fibrillation (AF), catheter based ablation uses high radio-frequency energy to eliminate potential sources of abnormal electrical events, especially around the ostia of pulmonary veins (PV). Fusing a patient-specific left atrium (LA) model (including LA chamber, appendage, and PVs) with electro-anatomical maps or overlaying the model onto 2-D real-time fluoroscopic images provides valuable visual guidance during the intervention. In this work, we present a fully automatic LA segmentation system on nongated C-arm computed tomography (C-arm CT) data, where thin boundaries between the LA and surrounding tissues are often blurred due to the cardiac motion artifacts. To avoid segmentation leakage, the shape prior should be exploited to guide the segmentation. A single holistic shape model is often not accurate enough to represent the whole LA shape population under anatomical variations, e.g., the left common PVs vs. separate left PVs. Instead, a part based LA model is proposed, which includes the chamber, appendage, four major PVs, and right middle PVs. Each part is a much simpler anatomical structure compared to the holistic one and can be segmented using a model-based approach (except the right middle PVs). After segmenting the LA parts, the gaps and overlaps among the parts are resolved and segmentation of the ostia region is further refined. As a common anatomical variation, some patients may contain extra right middle PVs, which are segmented using a graph cuts algorithm under the constraints from the already extracted major right PVs. Our approach is computationally efficient, taking about 2.6 s to process a volume with 256\,\times\,256\,\times\,245 voxels. Experiments on 687 C-arm CT datasets demonstrate its robustness and state-of-the-art segmentation accuracy. [ABSTRACT FROM AUTHOR]

• First work to investigate solutions for collaborative medical AI model for COVID-19. • A novel federated semi-supervised learning technique is proposed to train a network. • Method fully utilizes available data from different sources, w/ or w/o annotations. • Multinational dataset of 1704 scans from 3 countries (China, Italy, and Japan). • Studies fully evaluate and understand different configurations of the method. [Display omitted] The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, chest Computed Tomography (CT) has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis with chest CT scan, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. A common way to alleviate this issue is to fine-tune the model locally with the target domains local data and annotations. Unfortunately, the availability and quality of local annotations usually varies due to heterogeneity in equipment and distribution of medical resources across the globe. This impact may be pronounced in the detection of COVID-19, since the relevant patterns vary in size, shape, and texture. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing. [ABSTRACT FROM AUTHOR]

Purpose: In multiphase coronary CT angiography (CTA), a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low‐dose phases is significantly degraded. Recently, deep neural network approaches based on supervised learning technique have demonstrated impressive performance improvement over conventional model‐based iterative methods for low‐dose CT. However, matched low‐ and routine‐dose CT image pairs are difficult to obtain in multiphase CT. To address this problem, we aim at developing a new deep learning framework. Method: We propose an unsupervised learning technique that can remove the noise of the CT images in the low‐dose phases by learning from the CT images in the routine dose phases. Although a supervised learning approach is not applicable due to the differences in the underlying heart structure in two phases, the images are closely related in two phases, so we propose a cycle‐consistent adversarial denoising network to learn the mapping between the low‐ and high‐dose cardiac phases. Results: Experimental results showed that the proposed method effectively reduces the noise in the low‐dose CT image while preserving detailed texture and edge information. Moreover, thanks to the cyclic consistency and identity loss, the proposed network does not create any artificial features that are not present in the input images. Visual grading and quality evaluation also confirm that the proposed method provides significant improvement in diagnostic quality. Conclusions: The proposed network can learn the image distributions from the routine‐dose cardiac phases, which is a big advantage over the existing supervised learning networks that need exactly matched low‐ and routine‐dose CT images. Considering the effectiveness and practicability of the proposed method, we believe that the proposed can be applied for many other CT acquisition protocols. [ABSTRACT FROM AUTHOR]

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-mmdemonstrated excellent agreement with CSCTmanual(all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mmthan 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-mmthan with LDCT2.5-mm. [ABSTRACT FROM AUTHOR]

Objectives: To validate an artificial intelligence (AI)–based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)–gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. Methods: This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. Results: CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998–0.999 and 0.989, 95% CI 0.987–0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918–0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748–0.924) on LDCT. Conclusions: The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. Key Points: • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score–based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT. [ABSTRACT FROM AUTHOR]

Background: Hemifacial microsomia (HFM) is a congenital craniofacial malformation, and its features include hypoplasia and asymmetry in skeletal tissue and soft tissue. These features are usually associated with a difficult airway. Mandibular distraction osteogenesis can achieve mandibular advancement, but its effect on the subsequent laryngoscopic views has not been adequately documented. Aim: The aim of this study was to determine the change in laryngoscopic views after mandibular distraction osteogenesis (MDO) in children with HFM and to examine the radiological changes after MDO by using computed tomography (CT)‐based three‐dimensional (3‐D) airway reconstruction. Methods: An observational prospective study was carried out in children with HFM undergoing MDO. All children underwent 3‐D airway reconstruction preoperatively before the placement and removal of the distractor. The temporomandibular joint lesions in children were classified by the Pruzansky–Kaban classification. The Cormack–Lehane grade was assessed after induction under direct laryngoscopy. The relationship between difficult laryngoscopy and the Pruzansky–Kaban classification was assessed. Changes in the upper pharyngeal airway parameters and laryngoscopic views were analyzed. Results: Forty children (ages ranging from 5 to 16 years, males = 26, females = 14) with HFM underwent 80 anesthesia for MDO. The incidence of difficult laryngoscopy before mandibular distraction was 5/12 (41.7%) in type IIa, 19/24 (79.2%) in type IIb and 1/4 (25%) in type III HFM. The mean difference in the incidence of difficult laryngoscopy before and after MDO was 33.3% (95% CI: −6.7% to 73.3%), 50.0% (95% CI: 8.4% to 91.6%), and 0% for type IIa, type IIb, and type III, respectively. In type IIb, 12/19 cases of difficult laryngoscopy became easy after mandibular distraction: this was associated with an increase in the palatopharyngeal and glossopharyngeal CT parameters (p <.05). In type IIa, 5/5 cases of difficult laryngoscopy became easy after mandibular distraction, and 1/7 cases of easy laryngoscopy became difficult laryngoscopy. However, there were no significant changes in the palatopharyngeal, glossopharyngeal, and hypopharyngeal CT parameters in type IIa and type III HFM (p >.05). Conclusions: MDO can improve the laryngoscopic views in children with HFM. According to the Pruzansky–Kaban classification, children with type IIb showed the highest incidence of difficult laryngoscopy before MDO. After MDO, the improvement in laryngoscopic views under direct laryngoscopy in type IIb was related to the increase in the palatopharyngeal and glossopharyngeal volume. [ABSTRACT FROM AUTHOR]

Aortic valve calcium scoring by cardiac computed tomographic (CT) has been recommended as an alternative to classify the AS (aortic stenosis) severity, but it is unclear that whether CT findings would have additional value to discriminate significant AS subtypes including high gradient severe AS, classic low-flow, low gradient (LF-LG) AS, paradoxical LF-LG AS, and moderate AS. In this study, we examined the preoperative clinical and cardiac CT findings of different subtypes of AS in patients with surgical aortic valve replacement (AVR) and evaluated the subtype classification as a factor affecting post-surgical outcomes. This study included 511 (66.9 ± 8.8 years, 55% men) consecutive patients with severe AS who underwent surgical AVR. Aortic valve area (AVA) was obtained by echocardiography (AVAecho) and by CT (AVACT) using each modalities measurement of the left ventricular outflow tract. Patients with AS were classified as (1) high-gradient severe (n = 438), (2) classic LF-LG (n = 18), and (3) paradoxical LF-LG (n = 55) based on echocardiography. In all patients, 455 (89.0%) patients were categorized as severe AS according to the AVACT. However, 56 patients were re-classified as moderate AS (43 [9.8%] high-gradient severe AS, 5 [27.8%] classic LF-LG AS, and 8 [14.5%] paradoxical LF-LG AS) by AVACT. The classic LF-LG AS group presented larger AVACT and aortic annulus than those in high-gradient severe AS group and one third of them had AVACT ≥ 1.2 cm2. After multivariable adjustment, old age (hazard ratio [HR], 1.04, P = 0.049), high B-type natriuretic peptide (BNP) (HR, 1.005; P < 0.001), preoperative atrial fibrillation (HR, 2.75; P = 0.003), classic LF-LG AS (HR, 5.53, P = 0.004), and small aortic annulus on CT (HR, 0.57; P = 0.002) were independently associated with major adverse cardiac and cerebrovascular events (MACCE) after surgical AVR. [ABSTRACT FROM AUTHOR]

Objectives: The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2. Methods: Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression. Results: Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms. Conclusions: COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure. Key Points: • Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1–5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms. [ABSTRACT FROM AUTHOR]

Abstract Background We aimed to compare the myocardial ischemic burden assessed using semi-quantitative and quantitative analysis of computed tomography-myocardial perfusion imaging (CT-MPI) with that of single-photon emission computed tomography (SPECT). Methods From 2011 to 2013, 97 patients who underwent CT-MPI and SPECT were evaluated. The extent and severity of perfusion defects were assessed on a 5-point scale using a standard 16-segment model, and were expressed as summed stress score (SSS) and summed difference score (SDS). Receiver operating characteristic (ROC) curves for quantitative parameters were generated for the diagnosis of abnormal perfusion defect (SSS ≥ 4) and presence of ischemia (SDS ≥ 2) on SPECT. Results On CT-MPI, 298 (19.2%) of the 1552 segments showed perfusion abnormalities during stress, whereas perfusion abnormalities were shown in 179 (11.5%) segments on SPECT-MPI. On a per-person basis, there was good agreement, with intraclass correlation coefficients of 0.78 for SSS and 0.72 for SDS. A significant reduction of attenuation in stress and myocardial perfusion reserve index, along with an increase in % defect volume of CT-MPI, were demonstrated as the degree of perfusion defect or ischemia on SPECT increased. On the ROC curves, % defect volume on CT-MPI demonstrated the highest area under the curve: 0.91 for abnormal perfusion defect and 0.89 (all p < 0.001) for the presence of ischemia on SPECT. Conclusions Semi-quantitative analysis of CT-MPI showed good accordance with SPECT. A quantitative approach for CT-MPI, especially % defect volume, may provide additional value in the identification of myocardial perfusion abnormalities. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT01696006. Highlights • CT-MPI showed good agreement with SPECT by a semi-quantitative approach. • The % defect volume demonstrated excellent accuracy in the detection of perfusion abnormality on SPECT. • The % defect volume may play an additional role to identify myocardial perfusion defect and ischemia. [ABSTRACT FROM AUTHOR]

Background and Aim We aimed to investigate the ability of fluoro-2-deoxy- d-glucose (FDG) positron emission tomography (PET)/computed tomography (CT) to detect synchronous neoplasms, specifically obstructive colorectal cancer (CRC) and CRC in the proximal colon and to suggest a management strategy based on FDG PET/CT findings. Methods From the CRC surgery database of our institution, 518 patients with obstructive CRC whose proximal colon could not be examined by colonoscopy and who underwent preoperative FDG PET/CT were eligible for this study. Of these, final analyses were performed in 345 patients who had reference standards for the proximal colon, which were a surgical colectomy specimen and/or postsurgical colonoscopy. The per-patient and per-lesion performances of FDG PET/CT for synchronous CRC diagnosis were determined. Results Of 345 patients, 14 (4.1%) had 14 proximal synchronous CRCs. Thirty-four patients showed 39 areas of abnormal FDG uptake on PET/CT in the colon proximal to the obstructive CRC. PET/CT detected all of the 14 proximal synchronous CRCs. The per-patient PET/CT sensitivity, specificity, positive predictive value, and negative predictive value for proximal synchronous CRC were 100%, 93.9%, 41.2%, and 100%, respectively. Per-lesion values were 100%, 92.6%, 35.9%, and 100%, respectively. The per-lesion sensitivity and negative predictive value of PET/CT for advanced adenoma were 45.5% and 92.7%, respectively. Conclusions The FDG PET/CT shows a high sensitivity and negative predictive value for the detection of proximal synchronous CRC in patients with obstructive CRC, enabling negative findings in the proximal colon on PET/CT to definitively exclude proximal synchronous CRC. Preoperative PET/CT recommended to determine the proper surgical plan in patients with obstructive CRC. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the efficacy and safety of CyberKnife® treatment for locally-advanced pancreatic cancer (LAPC). Methods: The efficacy of CyberKnife® treatment was analyzed in 59 LAPC patients treated between October 2006 and September 2014. The median tumor volume was 27.1 mL (13.0- 125.145 mL). The median prescribed dose was 45 Gy (35-50 Gy), delivered in 5 fractions (3-8 fractions). The overall survival (OS) rates and freedom from local progression (FFLP) rates were estimated using the Kaplan-Meier survival curve. Results: The median follow-up for all patients was 10.9 months (3.2-48.7 months) and 15.6 months (3.9-37.6 months) among surviving patients. The median OS was 12.5 months, and the 1-year and 2-year survival rates were 53.9% and 35.1%, respectively. The 1-year FFLP rate was 90.8% based on the computed tomography (CT) evaluation. Grade 1-2 acute and late-stage gastrointestinal (GI) reactions were observed in 61% of the patients. One patient experienced grade 3 toxicity. Conclusion: Excellent clinical efficacy was obtained after treatment of LAPC using CyberKnife®, with minimal toxicity. [ABSTRACT FROM AUTHOR]

• Cycle-consistent adversarial denoising for unsupervised cardiac CT data. • Wavelet-based preprocessing to disentangle noise components from images. • Effective noise reduction of 4% extreme-low-dose CT images without creating artifacts. • Extensive evaluation of denoised image quality on a data set of 149 patients. [Display omitted] In electrocardiography (ECG) gated cardiac CT angiography (CCTA), multiple images covering the entire cardiac cycle are taken continuously, so reduction of the accumulated radiation dose could be an important issue for patient safety. Although ECG-gated dose modulation (so-called ECG pulsing) is used to acquire many phases of CT images at a low dose, the reduction of the radiation dose introduces noise into the image reconstruction. To address this, we developed a high performance unsupervised deep learning method using noise disentanglement that can effectively learn the noise patterns even from extreme low dose CT images. For noise disentanglement, we use a wavelet transform to extract the high-frequency signals that contain the most noise. Since matched low-dose and high-dose cardiac CT data are impossible to obtain in practice, our neural network was trained in an unsupervised manner using cycleGAN for the extracted high frequency signals from the low-dose and unpaired high-dose CT images. Once the network is trained, denoised images are obtained by subtracting the estimated noise components from the input images. Image quality evaluation of the denoised images from only 4% dose CT images was performed by experienced radiologists for several anatomical structures. Visual grading analysis was conducted according to the sharpness level, noise level, and structural visibility. Also, the signal-to-noise ratio was calculated. The evaluation results showed that the quality of the images produced by the proposed method is much improved compared to low-dose CT images and to the baseline cycleGAN results. The proposed noise-disentangled cycleGAN with wavelet transform effectively removed noise from extreme low-dose CT images compared to the existing baseline algorithms. It can be an important denoising platform for low-dose CT. [ABSTRACT FROM AUTHOR]

Balloon-assisted enteroscopy (BAE) is an important diagnostic modality for ongoing obscure gastrointestinal bleeding (OGIB). However, it is difficult to determine the optimal insertion route. We retrospectively analyzed the records of patients with OGIB contained in a multicenter enteroscopy database of 1108 balloon-assisted enteroscopy (BAE) procedures (875 patients) to find out factors affecting BAE route selection in patients with OGIB. A total of 603 BAE procedures in 512 patients were investigated: there were 392 (65.0%) bidirectional and 211 (35.0%) unidirectional procedures. Overt OGIB was more frequent in the latter group (p = 0.024). Computed tomography (CT) was more frequently performed in the unidirectional group (p < 0.001). Capsule endoscopy and a small bowel barium study were performed more frequently in the bidirectional group (p < 0.001 and p = 0.039, respectively). Multivariate analysis showed that occult OGIB, capsule endoscopy and a small bowel barium study were independently associated with use of the bidirectional approach (p = 0.011, p = 0.013 and p = 0.046, respectively). Conversely, CT was associated with use of the unidirectional approach (p < 0.001). Conclusion: CT can aid the selection of an optimal insertion route in OGIB patients. However, capsule endoscopy and small bowel barium study are unhelpful. [ABSTRACT FROM AUTHOR]

• A total of 374 multi-detector CT scans are made available to the research community, the biggest such dataset on spine until date (VerSe '19: https://osf.io/nqjyw/; VerSe '20: https://osf.io/t98fz/). • The VerSe benchmark includes annotations for two fundamental processing tasks, namely vertebrae labelling and segmentation. • Twenty-six fully-automated algorithms (eleven for VerSe '19, thirteen for VerSe '20, and one baseline) are benchmarked on this dataset, with the top performing algorithm achieving a mean vertebrae identification rate of 96.6% and a Dice coefficient of 91.7% in VerSe '20. • Further insights into these algorithms are provided by examining them at various levels of granularity ranging from dataset-level experiments to vertebrae-level performances to a field-of-view-related analysis. [Display omitted] Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe : the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse. [ABSTRACT FROM AUTHOR]