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The cardiothoracic ratio (CTR) is commonly assessed on chest radiography for detection of cardiac chamber enlargement, but the traditional cutpoint of 0.5 has low specificity. We sought to evaluate the diagnostic accuracy of new measurement techniques for the detection of cardiac enlargement on chest radiographs. We obtained retrospective cross-sectional data on consecutive patients who underwent both chest radiography and cardiac magnetic resonance imaging (MRI) within a 14-day interval between 2006 and 2016 at a large academic hospital network. We established the presence of cardiac chamber enlargement using cardiac MRI as the reference standard. We evaluated the diagnostic performance of different techniques for measuring heart size and CTR on frontal chest radiographs. Of 152 patients included, 81 (53%) were men and the mean age was 52 years. Maximum heart diameter had the highest area under the receiver operating characteristic curve for detection of cardiac enlargement (0.827, 95% confidence interval 0.760–0.894). In the subgroup of posteroanterior chest radiography studies (n = 101), a CTR cutpoint of 0.50 had only moderate sensitivity (72%) and specificity (72%). In men, a maximum heart diameter cutpoint of 15 cm had a sensitivity of 86% and a negative likelihood ratio of 0.24, and a cutpoint of 19 cm had a specificity of 100% and a positive likelihood ratio of infinity. In women, a maximum heart diameter cutpoint of 13 cm had a sensitivity of 91% and a negative likelihood ratio of 0.15, and a cutpoint of 17 cm had a specificity of 91% and a positive likelihood ratio of 3.5. A traditional CTR cutpoint of 0.5 has limited diagnostic value. Simple heart diameter measurements have higher diagnostic performance measures than CTR.

Background Cardiac involvement is common and is the leading cause of mortality in Fabry disease (FD). We explored the association between cardiovascular magnetic resonance (CMR) myocardial strain, T1 mapping, late gadolinium enhancement (LGE) and left ventricular hypertrophy (LVH) in patients with FD. Methods In this prospective study, 38 FD patients (45.0 ± 14.5 years, 37% male) and 8 healthy controls (40.1 ± 13.7 years, 63% male) underwent 3 T CMR including cine balanced steady-state free precession (bSSFP), LGE and modified Look-Locker Inversion recovery (MOLLI) T1 mapping. Global longitudinal (GLS) and circumferential (GCS) strain and base-to-apex longitudinal strain (LS) and circumferential strain (CS) gradients were derived from cine bSSFP images using feature tracking analysis. Results Among FD patients, 8 had LVH (FD LVH+, 21%) and 17 had LGE (FD LGE+, 45%). Nineteen FD patients (50%) had neither LVH nor LGE (FD LVH- LGE-). None of the healthy controls had LVH or LGE. FD patients and healthy controls did not differ significantly with respect to GLS (− 15.3 ± 3.5% vs. − 16.3 ± 1.5%, p  = 0.45), GCS (− 19.4 ± 3.0% vs. -19.5 ± 2.9%, p  = 0.84) or base-to-apex LS gradient (7.5 ± 3.8% vs. 9.3 ± 3.5%, p  = 0.24). FD patients had significantly lower base-to-apex CS gradient (2.1 ± 3.7% vs. 6.5 ± 2.2%, p  = 0.002) and native T1 (1170.2 ± 37.5 ms vs. 1239.0 ± 18.0 ms, p  < 0.001). Base-to-apex CS gradient differentiated FD LVH- LGE- patients from healthy controls (OR 0.42, 95% CI: 0.20 to 0.86, p  = 0.019), even after controlling for native T1 (OR 0.24, 95% CI: 0.06 to 0.99, p  = 0.049). In a nested logistic regression model with native T1, model fit was significantly improved by the addition of base-to-apex CS gradient (χ 2 (df = 1) = 11.04, p  < 0.001). Intra- and inter-observer agreement were moderate to good for myocardial strain parameters: GLS (ICC 0.849 and 0.774, respectively), GCS (ICC 0.831 and 0.833, respectively), and base-to-apex CS gradient (ICC 0.737 and 0.613, respectively). Conclusions CMR reproducibly identifies myocardial strain abnormalities in FD. Loss of base-to-apex CS gradient may be an early marker of cardiac involvement in FD, with independent and incremental value beyond native T1.

In this study, we propose a novel hybrid framework for assessing the invasiveness of an in-house dataset of 114 pathologically proven lung adenocarcinomas presenting as subsolid nodules on Computed Tomography (CT). Nodules were classified into group 1 (G1), which included atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinomas, and group 2 (G2), which included invasive adenocarcinomas. Our approach includes a three-way Integration of Visual, Spatial, and Temporal features with Attention, referred to as I-VISTA, obtained from three processing algorithms designed based on Deep Learning (DL) and radiomic models, leading to a more comprehensive analysis of nodule variations. The aforementioned processing algorithms are arranged in the following three parallel paths: (i) The Shifted Window (SWin) Transformer path, which is a hierarchical vision Transformer that extracts nodules’ related spatial features; (ii) The Convolutional Auto-Encoder (CAE) Transformer path, which captures informative features related to inter-slice relations via a modified Transformer encoder architecture; and (iii) a 3D Radiomic-based path that collects quantitative features based on texture analysis of each nodule. Extracted feature sets are then passed through the Criss-Cross attention fusion module to discover the most informative feature patterns and classify nodules type. The experiments were evaluated based on a ten-fold cross-validation scheme. I-VISTA framework achieved the best performance of overall accuracy, sensitivity, and specificity (mean ± std) of 93.93 ± 6.80%, 92.66 ± 9.04%, and 94.99 ± 7.63% with an Area under the ROC Curve (AUC) of 0.93 ± 0.08 for lung nodule classification among ten folds. The hybrid framework integrating DL and hand-crafted 3D Radiomic model outperformed the standalone DL and hand-crafted 3D Radiomic model in differentiating G1 from G2 subsolid nodules identified on CT.

The coronary sinus (CS) is an important vascular structure that allows for access into the coronary veins in multiple interventional cardiology procedures, including catheter ablation of arrhythmias, pacemaker implantation and retrograde cardioplegia. The success of these procedures is facilitated by the knowledge of the CS anatomy, in particular the recognition of its variants and anomalies. This pictorial essay reviews the spectrum of CS anomalies, with particular attention to the distinction between clinically benign variants and life-threatening defects. Emphasis will be placed on the important role of cardiac CT and cardiovascular magnetic resonance in providing detailed anatomic and functional information of the CS and its relationship to surrounding cardiac structures. Teaching Points • Cardiac CT and cardiovascular magnetic resonance offer 3D high-resolution mapping of the coronary sinus in pre-surgical planning . • Congenital coronary sinus enlargement occurs in the presence or absence of a left-to-right shunt . • Lack of recognition of coronary sinus anomalies can lead to adverse outcomes in cardiac procedures . • In coronary sinus ostial atresia, coronary venous drainage to the atria occurs via Thebesian or septal veins . • Coronary sinus diverticulum is a congenital outpouching of the coronary sinus and may predispose to cardiac arrhythmias .

Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre-defined and extracted without taking the desired target into account. Furthermore, accurate segmentation of the tumor is required for development of a reliable predictive model, which may be objective and a time-consuming task. To address these drawbacks, we propose a deep learning-based radiomics model for the time-to-event outcome prediction, referred to as DRTOP that takes raw images as inputs, and calculates the image-based risk of death or recurrence, for each patient. Our experiments on an in-house dataset of 132 lung cancer patients show that the obtained image-based risks are significant predictors of the time-to-event outcomes. Computed Tomography (CT)-based features are predictors of the overall survival (OS), with the hazard ratio (HR) of 1.35, distant control (DC), with HR of 1.06, and local control (LC), with HR of 2.66. The Positron Emission Tomography (PET)-based features are predictors of OS and recurrence free survival (RFS), with hazard ratios of 1.67 and 1.18, respectively. The concordance indices of 68 % , 63 % , and 64 % for predicting the OS, DC, and RFS show that the deep learning-based radiomics model is as accurate or better in predicting predefined clinical outcomes compared to hand-crafted radiomics, with concordance indices of 51 % , 64 % , and 47 % , for predicting the OS, DC, and RFS, respectively. Deep learning-based radiomics has the potential to offer complimentary predictive information in the personalized management of lung cancer patients.

Bridging bronchi are the rarest of the major airway anomalies reported in the literature. In this brief report, we present a case of a symptomatic adult male patient presenting with a type 2 bridging bronchus associated with left pulmonary artery sling.

109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen’s Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.

Highlights of this retrospective study of adults with repaired truncus arteriosus (rTA) and contemporary cardiovascular magnetic resonance imaging studies (CMR) include:•CMR measures are stratified according to anatomic subtype in rTA•Right ventricular mass is increased in rTA as compared with normal•Right ventricular function and aortic size in rTA are associated with outcomes The cardiovascular magnetic resonance imaging (CMR) features of adults with repaired truncus arteriosus (rTA) are largely undefined. We sought to explore CMR characteristics in rTA and to identify associations between imaging findings and cardiovascular outcomes. Adults with rTA and CMR were identified and anatomic subtypes (1-4) were assigned (Collett and Edwards classification). CMR characteristics, clinical data at last follow-up and adverse cardiovascular outcome were recorded. Twenty-seven adults (19% male) were studied (median age at cardiovascular magnetic resonance 26 years [interquartile range 18 to 40]) over 5.2-year duration [interquartile range 2.5 to 7.5]. With the exception of mildly increased RV mass (30 ± 12 g/m2), cardiac chamber measurements were within the normal range. In CMR measurements, only pulmonary artery peak velocity differed in subtypes (highest in subtype 3, 318 ± 26 cm/s, p = 0.029). Number of cardiovascular interventions in adulthood was moderately correlated with left ventricular end-diastolic volume (r = 0.463, p = 0.015), left ventricular ejection fraction (r = 0.425, p = 0.027) and neoaortic root size (r = 0.398, p = 0.039). Cardiovascular events (nonmutually exclusive) in 5 of 27 patients (19%) included death (n = 1), heart failure (n = 1), ventricular tachycardia (n = 1), and atrial tachycardia (n = 3). Increased cardiovascular risk was associated with decreased right ventricular ejection fraction (odds ratio 1.153, confidence interval 1.003 to 1.326, p = 0.046) and smaller ascending aorta diameter (odds ratio 1.758, confidence interval 1.037 to 2.976, p = 0.036). In conclusion, decreased right ventricular ejection fraction and smaller ascending aorta on cardiovascular magnetic resonance were associated with adverse events in rTA.

The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.