RATIONALE AND OBJECTIVES:Pericoronary adipose tissue (PCAT) is a key cardiovascular risk biomarker, yet its temporal changes after heart transplantation (HT) and comparison with controls remain unclear. This study investigates the temporal changes of PCAT in stable HT recipients and compares it to controls. MATERIALS AND METHODS:In this study, we analyzed 159 stable HT recipients alongside two control groups. Both control groups were matched to a subgroup of HT recipients who did not have coronary artery stenosis. Group 1 consisted of 60 individuals matched for age, sex, and body mass index (BMI), with no history of hypertension, diabetes, hyperlipidemia, or smoking. Group 2 included 56 individuals additionally matched for hypertension, diabetes, hyperlipidemia, and smoking history. PCAT volume and fat attenuation index (FAI) were measured using AI-based software. Temporal changes in PCAT were assessed at multiple time points in HT recipients, and PCAT in the subgroup of HT recipients without coronary stenosis was compared to controls. RESULTS:Stable HT recipients exhibited a progressive decrease in FAI and an increase in PCAT volume over time, particularly in the first five years post-HT. Similar trends were observed in the subgroup of HT recipients without coronary stenosis. Compared to controls, PCAT FAI was significantly higher in the HT subgroup during the first five years post-HT (P < 0.001). After five years, differences persisted but diminished, with no statistically significant differences observed in the PCAT of left anterior descending artery (LAD) (P > 0.05). A negative correlation was observed between FAI and PCAT volume post-HT (r = - 0.75 ∼ - 0.53). CONCLUSIONS:PCAT volume and FAI undergo temporal changes in stable HT recipients, especially during the first five years post-HT. Even in HT recipients without coronary stenosis, PCAT FAI differs from controls, indicating distinct changes in this cohort.

Lymph node status is a key factor in determining stage, treatment, and prognosis in cancers. Small lymph nodes in fat-rich gastrointestinal and breast cancer specimens are easily missed in conventional sampling methods. This study examined the effectiveness of the degreasing pretreatment with dimethyl sulfoxide (DMSO) in lymph node detection and its impact on the analysis of clinical treatmenterelated proteins and molecules. Thirty-three cases of gastrointestinal cancer specimens from radical gastrectomy and 63 cases of breast cancer specimens from modified radical mastectomy were included. After routine sampling of lymph nodes, the specimens were immersed in DMSO for 30 minutes for defatting. We assessed changes in the number of detected lymph nodes and pN staging in 33 gastrointestinal cancer specimens and 37 breast cancer specimens. In addition, we analyzed histologic characteristics, Masson trichrome special staining, and immunohistochemistry (gastrointestinal cancer: MMR, HER2, and PD-L1; breast cancer: ER, PR, AR, HER2, Ki-67, and PD-L1). Molecular status was evaluated for colorectal cancer (KRAS, NRAS, BRAF, and microsatellite instability) and breast cancer (HER2) in gastrointestinal cancer specimens and the remaining 26 breast cancer specimens. Compared with conventional sampling, DMSO pretreatment increased the detection rate of small lymph nodes (gastrointestinal cancer: P < .001; breast cancer: P < .001) and improved pN staging in 1 case each of gastric cancer, colon cancer, and rectal cancer (3/33; 9.1%). No significant difference in the morphology, special staining, protein, and molecular status of cancer tissue after DMSO treatment was found. Based on these results and our institutional experience, we recommend incorporating DMSO degreasing pretreatment into clinical pathologic sampling practices. (c) 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Purpose Both of extracellular extravascular volume (EEV) and extracellular volume fraction (ECV) were proposed to quantify enlargement of myocardial interstitial space due to myocardium loss or fibrosis. The study aimed to investigate the feasibility of using EEV derived from myocardial computed tomography (CT) perfusion imaging (VPCT) and extracellular volume quantification with single-energy subtraction CT (ECV − SECT ) for quantifying myocardial fibrosis. Methods In this study, 17 patients with suspected and known coronary artery disease underwent examination using a dual-source CT scanner. The EEV − VPCT was derived from dynamic whole-heart myocardial perfusion imaging, and the ECV _SECT was calculated from late-enhanced images 5 min after bolus contrast injection by subtracting the noncontrast baseline. The late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) imaging was used as a reference. Results In total, 11 patients and 73 segments exhibited positivity for LGE on CMR imaging. These were classified into three groups according to the segments: fibrotic segments (group I, n = 73), nonfibrotic segments in LGE-positive patients (group II, n = 103), and segments in LGE-negative patients (group III, n = 80). ECV − SECT , EEV − VPCT , myocardial blood flow (MBF), and myocardial blood volume (MBV) significantly differed among these groups (all P < 0.05). ECV − SECT was significantly higher and EEV − VPCT , MBF, and MBV were significantly lower in fibrotic myocardial segments than in nonfibrotic ones (all P < 0.01). ECV − SECT and EEV − VPCT independently affected myocardial fibrosis. There was no significant correlation between ECV − SECT and EEV − VPCT . The capability of EEV − VPCT to diagnose myocardial fibrosis was equivalent to that of ECV − SECT (area under the curve: 0.798 vs. 0.806, P = 0.844). ECV − SECT of > 41.2% and EEV − VPCT of < 10.3% indicated myocardial fibrosis. Conclusions EEV − VPCT is actually first-pass distribution volume that can feasibly be used to quantify myocardial fibrosis. Furthermore, the diagnostic efficacy of EEV − VPCT is comparable to that of ECV − SECT .

In this study, a novel CdSe@PEI-PVP luminescent hydrogel was constructed by using MPA-CdSe quantum dots (QDs) as a single emitter and hydrogen bond cross-linking network between highly branched polyethyleneimine (PEI) and polyvinylpyrrolidone (PVP) as a hydrogel skeleton, in which PEI also works as a passivation ligand for MPA-CdSe quantum dots. Utilizing the dual emission of MPA-CdSe quantum dots and adjusting the surface state of MPA-CdSe with PEI and Cd2+, CdSe@PEI-PVP hydrogel can emit bright white light as a whole. Furthermore, CdSe@PEI-PVP shows specific recognition for Cu2+, luminescent color changes from bright white to orange when Cu2+ is added, which enables CdSe@PEI-PVP hydrogel to serve as a fluorescent probe for ion detection. The luminescent hydrogel has great self-healing ability and reversible morphological thermal responsiveness, existing as a white luminescent hydrogel at room temperatures and transforming into a pale green luminescent solution at high temperatures.

To evaluate myocardial strain in patients with different coronary artery disease-reporting and data system (CAD-RADS) levels using the computed tomography (CT) feature tracking technology and to investigate the relationship of myocardial strain with coronary artery calcium scores (CACs) and the degree of coronary artery stenosis. We prospectively enrolled 237 consecutive patients to undergo coronary CT angiography. The participants were divided into the following groups: control (n = 87), CAD-RADS 1 (n = 43), CAD-RADS 2 (n = 43), CAD-RADS 3 (n = 38), and CAD-RADS 4 and above (n = 26). Myocardial strains were analyzed by commercial software, and CACs and coronary stenosis were assessed on post-processing stations. Differences between multiple groups were analyzed using one-way analysis of variance or the Kruskal–Wallis test. Logistic regression were used to analyze the effects of dichotomous variables. As the CAD-RADS level increased, the global circumferential strain (GCS), global longitudinal strain (GLS) and global radial strain (GRS) of the left ventricle based on CT gradually decreased. A significant correlation was observed between global myocardial strain and CACs (GRS: r = − 0.219, GCS: r = 0.189, GLS: r = 0.491; P < 0.05). The independent predictors of obstructive CAD were age ( β = 0.065, odds ratio [ OR ] = 1.067, P = 0.005), left ventricular ejection fraction ( β = 0.145, OR = 1.156, P = 0.047), and GLS ( β = 0.232, OR = 1.261, P = 0.01). CT-derived GLS of the left ventricle is correlated with CAD-RADS levels and CACs. It may be a better indicator than CACs to reflect the severity of CAD.

The artificial intelligence (AI) system has achieved expert-level performance in electrocardiogram (ECG) signal analysis. However, in underdeveloped countries or regions where the healthcare information system is imperfect, only paper ECGs can be provided. Analysis of real-world ECG images (photos or scans of paper ECGs) remains challenging due to complex environments or interference. In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images. The system was evaluated on a large dataset of 52,357 patients from multiple regions and populations across the world. On the detection task, the AI system obtained area under the receiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test 1), 0.984 (external test 2), and 0.979 (external test 3), respectively. Meanwhile, the detection results of AI system showed a strong correlation with the diagnosis of cardiologists (cardiologist 1 (R=0.794, p<1e-3), cardiologist 2 (R=0.812, p<1e-3)). On the screening task, the AI system achieved AUCs of 0.894 (hold-out test) and 0.850 (external test). The screening performance of the AI system was better than that of the cardiologists (AI system (0.846) vs. cardiologist 1 (0.520) vs. cardiologist 2 (0.480)). Our study demonstrates the feasibility of an accurate, objective, easy-to-use, fast, and low-cost AI system for CA detection and screening. The system has the potential to be used by healthcare professionals, caregivers, and general users to assess CAs based on real-world ECG images.

Objective To assess the diagnostic value of spectral parameters in differentiating adrenal adenomas from metastases based on dual-layer detector spectral CT (DLSCT). Materials and methods Patients with adenomas or metastases who underwent enhanced DLSCT of the adrenals were enrolled. The CT values of virtual noncontrast images (CT VNC ), iodine density (ID) values, and Z-effective (Z-eff) values, the normalized iodine density (NID) values, slopes of spectral HU curves (s-SHC), and iodine-to-CT VNC ratios of the tumors were measured in each phase. Receiver operating characteristic (ROC) curves were used to compare the diagnostic values. Results Ninety-nine patients with 106 adrenal lesions (63 adenomas, 43 metastases) were included. In the venous phase, all spectral parameters were significantly different between adenomas and metastases (all p < 0.05). The combined spectral parameters showed a better diagnostic performance in the venous phase than in other phase (P < 0.05). The iodine-to-CT VNC value had a larger area under the ROC curve (AUC) than the other spectral parameters in the differential diagnosis of adenomas and metastases, with a diagnostic sensitivity and specificity of 74.4% and 91.9%, respectively. In the differential diagnosis of lipid-rich adenomas, lipid-poor adenomas and metastases, the CT VNC value and s-SHC value also had a larger AUC than the other spectral parameters, with a diagnostic sensitivity of 97.7%, 79.1% and specificity of 91.2%, 93.1%, respectively. Conclusion On DLSCT, the combined spectral parameters in the venous phase could help better distinguish adrenal adenomas from metastases. The iodine-to-CT VNC , CT VNC and s-SHC values had the highest AUC values in differentiating adenomas, lipid-rich adenomas and lipid-poor adenomas from metastases, respectively.

Introduction and objectives: Artificial intelligence (AI) made it achievable that aortic dilation could be measured in CT images indirectly, while aortic diameter (AD) has the certain relationship with blood pressure. It was potential that the blood pressure condition be determined by AD measurement using the data obtained from a CT scanning especially in identifying masked hypertension and predicting the risk of poor control of blood pressure (BP) which was easy to elude diagnosis in clinic. We aimed to evaluate the possibility of utilizing AD by AI for predicting the risk of adverse BP status (including masked hypertension or poor BP control) and the optimal thoracic aortic position in measurement as well as the cutoff value for predicting the risk. Methods: Eight hundred and one patients were enrolled in our study. AI-Rad Companion Cardiovascular (K183268 FDA approved) was used to perform automatic aorta measurement in thoracic CT images at nine key positions based on AHA guidelines. Data was post processed by software from AI-Rad Companion undergone rigorous clinical validation by both FDA and CE as verification of its efficacy and usability. The AD's risk and diagnostic value was assessed in identifying hypertension in the general population, in identifying the poor BP controlled in the hypertension population, and in screening masked hypertension in the general population respectively by multiple regression analysis and receiver operating curve analysis. Results: AD measured by AI was a risk factor for adverse BP status after clinical covariates adjustment (OR = 1.02 similar to 1.26). The AD at mid descending aorta was mostly affected by BP particularly, which is optimal indicator in identifying hypertension in the general population (AUC = 0.73) and for screening masked hypertension (AUC = 0.78). Conclusion: Using AI to measure the AD of the aorta, particularly at the position of mid descending aorta, is greatly valuable for identifying people with poor BP status. It will be possible to reveal more clinical information reflected by ordinary CT images and enrich the screening methods for hypertension, especially masked hypertension.

(1) Background: To assess the efficacy of the quantitative parameters of intravoxel incoherent motion (IVIM) diffusion-weighted imaging for hepatocellular carcinoma (HCC) diagnosis after transarterial chemoembolization (TACE). (2) Methods: Fifty HCC patients after TACE were included and underwent MRI. All of the patients were scanned with the IVIM-DWI sequence and underwent TACE retreatment within 1 week. Referring to digital subtraction angiography (DSA) and MR enhanced images, two readers measured the f, D, and D* values of the tumor active area (TAA), tumor necrotic area (TNA), and adjacent normal hepatic parenchyma (ANHP). Then, the distinctions of the TAA, TNA, and ANHP were compared and we analyzed the differential diagnosis of the parameters in three tissues. (3) Results: For values of f and D, there were significant differences between any of the TAA, TNA, and ANHP (p < 0.05). The values of f and D were the best indicators for identifying the TAA and TNA, with AUC values of 0.959 and 0.955, respectively. The values of f and D performed well for distinguishing TAA from ANHP, with AUC values of 0.835 and 0.753, respectively. (4) Conclusions: Quantitative IVIM-DWI was effective for evaluating tumor viability in HCC patients treated with TACE and may be helpful for non-invasive monitoring of the tumor viability.

Abstract Background This study aimed to evaluate the artificial intelligence (AI)-based coronary artery calcium (CAC) quantification and regional distribution of CAC on non-gated chest CT, using standard electrocardiograph (ECG)-gated CAC scoring as the reference. Methods In this retrospective study, a total of 405 patients underwent non-gated chest CT and standard ECG-gated cardiac CT. An AI-based algorithm was used for automated CAC scoring on chest CT, and Agatston score on cardiac CT was manually quantified. Bland-Altman plots were used to evaluate the agreement of absolute Agatston score between the two scans at the patient and vessel levels. Linearly weighted kappa (κ) was calculated to assess the reliability of AI-based CAC risk categorization and the number of involved vessels on chest CT. Results The AI-based algorithm showed moderate reliability for the number of involved vessels in comparison to measures on cardiac CT (κ = 0.75, 95% CI 0.70–0.79, P < 0.001) and an assignment agreement of 76%. Considerable coronary arteries with CAC were not identified with a per-vessel false-negative rate of 59.3%, 17.8%, 34.9%, and 34.7% for LM, LAD, CX, and RCA on chest CT. The leading causes for false negatives of LM were motion artifact (56.3%, 18/32) and segmentation error (43.8%, 14/32). The motion artifact was almost the only cause for false negatives of LAD (96.6%, 28/29), CX (96.7%, 29/30), and RCA (100%, 34/34). Absolute Agatston scores on chest CT were underestimated either for the patient and individual vessels except for LAD (median difference: − 12.5, − 11.3, − 5.6, − 18.6 for total, LM, CX, and RCA, all P < 0.01; − 2.5 for LAD, P = 0.18). AI-based total Agatston score yielded good reliability for risk categorization (weighted κ 0.86, P < 0.001) and an assignment agreement of 86.7% on chest CT, with a per-patient false-negative rate of 15.2% (28/184) and false-positive rate of 0.5% (1/221) respectively. Conclusions AI-based per-patient CAC quantification on non-gated chest CT achieved a good agreement with dedicated ECG-gated CAC scoring overall and highly reliable CVD risk categorization, despite a slight but significant underestimation. However, it is challenging to evaluate the regional distribution of CAC without ECG-synchronization.

Objective: The objective was to evaluate the normal value of left ventricular myocardial strain using the computed tomography feature-tracking technique and to explore the correlation between myocardial strains and cardiac function parameters. Methods: Participants suspected of coronary heart disease were selected from 17 August 2020 to 5 November 2020 to undergo coronary computed tomography angiography using a third-generation dual-source CT scanner. Data were imported into a commercial software (Medis) after multiphase reconstruction. The cardiac function parameters, radial (Err), circumferential (Ecc), and longitudinal strain (Ell) of the left ventricle were recorded. Results: A total of 87 normal subjects were enrolled, including 41 males and 46 females. For healthy subjects, the global radial strain (GRS), circumferential strain (GCS), and longitudinal strain (GLS) of the left ventricle were 74.5 ± 15.2%, −22.7 ± 3.0%, and −26.6 ± 3.2%, respectively. The Err and Ecc absolute values (|Ecc|) were the largest at the apex, and the |Ell| gradually increased from the base to the apex. The Err and |Ecc| were the largest in the lateral and inferior wall, respectively. |Ell| showed a clockwise decrease from the lateral wall in the short axis. Meanwhile, the GRS and |GLS| in females were higher than that in males. Multiple linear regression analysis showed that both SV and LVEF were the independent determinants of GRS, GCS, and GLS. BMI and CO were the independent determined factors of GCS. Conclusions: At a reasonable radiation dose, CT feature-tracking is a feasible and reproducible method to analyze left ventricular myocardial strain. Left ventricular myocardial strain in normal subjects varies in gender, segments, levels, and regions.

BACKGROUND:In this study, our focus was on pulmonary sequelae of coronavirus disease 2019 (COVID-19). We aimed to develop and validate CT-based radiomic models for predicting the presence of residual lung lesions in COVID-19 survivors at three months after discharge.METHODS:We retrospectively enrolled 162 COVID-19 confirmed patients in our hospital (84 patients with residual lung lesions and 78 patients without residual lung lesions, at three months after discharge). The patients were all randomly allocated to a training set (n = 114) or a test set (n = 48). Radiomic features were extracted from chest CT images in different regions (entire lung or lesion) and at different time points (at hospital admission or at discharge) to build different models, sequentially, or in combination, as follows: (1) Lesion_A model (based on the lesion region at admission CT); (2) Lesion_D model (based on the lesion region at discharge CT); (3) Δlesion model (based on the lesion region at admission CT and discharge CT); (4) Lung_A model (based on the lung region at admission CT); (5) Lung_D model (based on the lung region at discharge CT); (6) Δlung model (based on the lung region at admission CT and discharge CT). The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the predictive performances of the radiomic models.RESULTS:Among the six models, the Lesion_D and the Δlesion models achieved better predictive efficacy, with AUCs of 0.907 and 0.927, sensitivity of 0.898 and 0.763, and specificity of 0.855 and 0.964 in the training set, and AUCs of 0.875 and 0.837, sensitivity of 0.920 and 0.680, and specificity of 0.826 and 0.913 in the test set, respectively.CONCLUSIONS:The CT-based radiomic models showed good predictive effects on the presence of residual lung lesions in COVID-19 survivors at three months after discharge, which may help doctors to plan follow-up work and to reduce the psychological burden of COVID-19 survivors.

Background: Arterial aging is characterized by decreased vascular function, caused by arterial stiffness (AS), and vascular morphological changes, caused by arterial dilatation. We analyzed the relationship of pre-AS and AS, as assessed by cardio ankle vascular index (CAVI), with arterial diameters (AD) at nine levels, from the aortic sinus to the abdominal aorta, as measured by artificial intelligence (AI) on non-enhanced chest computed tomography (CT) images.Methods: Overall, 801 patients who underwent both chest CT scan and arterial elasticity test were enrolled. Nine horizontal diameters of the thoracic aorta (from the aortic sinuses of Valsalva to the abdominal aorta at the celiac axis origin) were measured by AI using CT. Patients were divided into non-AS (mean value of the left and right CAVIs [M.CAVI] < 8), pre-AS (8 ≤ M.CAVI < 9), and AS (M.CAVI ≥ 9) groups. We compared AD differences among groups, analyzed the correlation of age, ADs, and M.CAVI or the mean pressure-independent CAVI (M.CAVI0), Furthermore, we evaluated the risk predictors and the diagnostic value of the nine ADs for pre-AS and AS.Results: The AD at mid descending aorta (MD) correlated strongest with CAVI (r = 0.46, p < 0.001) or M.CAVI0 (r = 0.42, p < 0.001). M.CAVI was most affected by the MD AD and by age. An increase in the MD AD independently predicted the occurrence of pre-AS or AS. For MD AD, every 4.37 mm increase caused a 14% increase in the pre-AS and AS risk and a 13% increase in the AS risk. With a cut-off value of 26.95 mm for the MD AD, the area under the curve (AUC) for identifying the risk of AS was 0.743. With a cut-off value of 25.15 mm, the AUC for identifying the risk of the stage after the prophase of AS is 0.739.Conclusions: Aging is associated with an increase in AD and a decrease in arterial elasticity. An increase in AD, particularly at the MD level is an independent predictor of AS development.

The prevalence of coronary artery anomalies ranges from 0.3% to 5.6% in studies on patients undergoing coronary angiography. The left anterior descending artery (LAD) continuing as posterior descending artery (PDA) is termed as ‘hyper-dominant LAD’ or ‘super-dominant LAD’. Jariwala et al. had reviewed only 17 reports of the hyper-dominant LAD until 2018. Here, we present a case of a patient who was found to have an atypical hyper-dominant LAD with an unusual long course that continued across the left ventricular apex as a PDA, thereafter formed into a sac after supplying the area of the diaphragmatic surface of the left ventricle (Figure 1A; Video 1). Due to the existence of the bulging sac, the wrapping PDA was mistaken for a collateral branch. In addition, a continuation of the LAD (distal-LAD) at the distal portion of the sac was visualized by the microcatheter angiography, which was also misinterpreted as the distal segment of the occluded vessel (Figure 1B; Video 2). Originally, we speculated that this may be a stump-less chronic total occlusion (CTO). The intravascular ultrasound was used to identify entry channels into CTO lesions. Yet, no obvious disruption and proximal stump in the right coronary artery was observed (Video 3). For a more accurate understanding of the vessel anatomy, coronary computed tomographic angiography (CCTA) was performed to visualize the coronaries. CCTA with three-dimensional reconstructions showed an unusual long course of the LAD, which wrapped around the apex of the heart, continued as a PDA up to the crux of the heart, thereafter formed into a sac after supplying the area of the diaphragmatic surface of the left ventricle, then terminated in the posterior atrioventricular groove (Figure 1C–F).

HomeRadiology: Cardiothoracic ImagingVol. 2, No. 1 Previous Images in Cardiothoracic ImagingFree AccessPulmonary ImagingSevere Acute Respiratory Disease in a Huanan Seafood Market Worker: Images of an Early CasualtyLijuan Qian, Jie Yu, Heshui Shi Lijuan Qian, Jie Yu, Heshui Shi Author AffiliationsFrom the Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.Address correspondence to H.S. (e-mail: [email protected]).Lijuan QianJie YuHeshui Shi Published Online:Feb 14 2020https://doi.org/10.1148/ryct.2020200033MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In A 44-year-old man who was a transportation staff member in the Huanan seafood market in Wuhan, China, presented with a 13-day history of high fever and cough on December 25, 2019. High-sensitivity C-reactive protein level and erythrocyte sedimentation rate were elevated (>160.0 mg/L and ≥78.6 mm/h, respectively), while blood cell count showed normal white cells (5.29 × 109/L) with decreased lymphocytes (0.22 × 109/L). Chest CT showed patchy bilateral ground-glass opacities with peribronchial and peripheral/subpleural distribution (Fig 1). Serial follow-up chest radiographs showed increased extension of the lung opacities and development of basilar predominant consolidation (Fig 2). He was clinically diagnosed with severe pneumonia and acute respiratory distress syndrome. Usual respiratory pathogens were excluded, and he was eventually diagnosed as a suspected case of COVID-2019 (formerly known as 2019 novel coronavirus) infection. Unfortunately, he died 1 week later after failure of supportive measures.Figure 1: Images in a 44-year-old man who presented with fever and suspected COVID-19 pneumonia. A-C, Thin-slice (1-mm) axial CT images showed multiple patchy ground-glass opacity along the peribronchial and subpleural lungs. Some reticular opacities were also found within areas of ground glass (crazy-paving pattern). Lymphadenopathy was absent. D-F, Multiplanar reconstruction showed diffuse distribution of lesions.Figure 1:Download as PowerPointOpen in Image Viewer Figure 2: Images in a 44-year-old man who presented with fever and suspected COVID-19 pneumonia. A-C, Serial chest radiographs spanning an interval of 4 days showed rapid progressively increased extension and density of the lung opacities, culminating in confluent basilar predominant bilateral lung consolidation.Figure 2:Download as PowerPointOpen in Image Viewer This case occurring at the epicenter outbreak of COVID-19 pneumonia illustrates the potential severity of this disease, at the same time that it underscores the role of imaging for monitoring disease progression. Moreover, CT could also have an important diagnostic role, especially when confirmatory tests, such as the real-time RT-PCR are unavailable (1–4).Disclosures of Conflicts of Interest: L.Q. disclosed no relevant relationships. J.Y. disclosed no relevant relationships. H.S. disclosed no relevant relationships.Keywords: CT, Infection, Lung Authors declared no funding for this work. References1. Pneumonia Treatment Program for New Coronary Virus Infection (Trial 5th Edition). National Health Commission of the People’s Republic of China Web site. http://www.nhc.gov.cn/. Published February 4, 2020. Updated February 4, 2020. Accessed February 4, 2020. Google Scholar2. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;6736(20):1–10. Google Scholar3. Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, Cui J, Xu W, Yang Y, Fayad Z, Jacobi A, Li K, Li S, Shan H. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology 2020 https://doi.org/10.1148/radiol.2020200230 (in press). Link, Google Scholar4. Kanne JP. Chest CT Findings in 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist. Radiology 2020 https://doi.org/10.1148/radiol.2020200241 (in press). 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Background Previous studies have found that impaired global myocardial systolic strain is associated with cardiovascular events in T2DM patients. However, the effect of hypertension (HT) on left atrial (LA), right ventricular (RV) and left ventricular (LV) myocardial deformation in hypertensive T2DM patients has not been fully studied by cardiac magnetic resonance feature tracking (CMR-FT). Our aim was to assess LA, RV and LV strain in T2DM patients with T2DM-HT and without hypertension using CMR-FT and to determine the underlying relationships with clinical parameters. Methods A total of 27 T2DM patients, 23 T2DM-HT patients and 31 controls were studied. LA, LV and RV strain was evaluated using CMR-FT. The clinical and biochemical parameters of the patients were collected. Results The T2DM patients had reduced LA global circumferential strain (LAGCS), radial strain (LAGRS), longitudinal strain (LAGLS) and right ventricular longitudinal strain (RVGLS) compared with the controls (LAGCS: 27.2 ± 2.1% vs 33.5 ± 2.4%; LAGRS: − 28.6 ± 1.1% vs − 31.9 ± 1.3%; LAGLS: 24.3 ± 1.3% vs 31.4 ± 1.5; RVGLS: − 21.4 ± 1.2% vs − 26.3 ± 1.1%, p < 0.05 for all). The T2DM-HT patients had greater LAGCS, LAGRS and LAGLS than the T2DM patients (LAGCS: 40.4 ± 3.8% vs 27.2 ± 2.1%; LAGRS: − 36.8 ± 2.0% vs − 28.6 ± 1.1%; LAGLS: 32.3 ± 2.4% vs 24.3 ± 1.3%, p < 0.05 for all). In the diabetic patients, LAGCS was associated with microalbuminuria levels (standardized ß = − 0.289, p = 0.021), and LAGCS, LAGRS and LAGLS were correlated with diuretic treatment (standardized ß =0.440, − 0.442, and 0.643, p < 0.05 for all). Conclusions CMR-FT may be considered a promising tool for the early detection of abnormal LA and RV myocardial strain. LA and RV strain values are impaired in T2DM patients. The amelioration of LA strain might be associated with hypertensive compensation or antihypertensive treatment, which requires to be confirmed in larger trials.

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