Search

Objectives: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. Methods: A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. Results: Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. Conclusions: The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. Key Points: • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung. [ABSTRACT FROM AUTHOR]

Objectives: To build models based on conventional logistic regression (LR) and machine learning (ML) algorithms combining clinical, morphological, and hemodynamic information to predict individual rupture status of unruptured intracranial aneurysms (UIAs), afterwards tested in internal and external validation datasets.Methods: Patients with intracranial aneurysms diagnosed by computed tomography angiography and confirmed by invasive cerebral angiograph or clipping surgery were included. The prediction models were developed based on clinical, aneurysm morphological, and hemodynamic parameters by conventional LR and ML methods.Results: The training, internal validation, and external validation cohorts were composed of 807 patients, 200 patients, and 108 patients, respectively. The area under curves (AUCs) of conventional LR models 1 (clinical), 2 (clinical and aneurysm morphological), and 3 (clinical, aneurysm morphological and hemodynamic characteristics) were 0.608, 0.765, and 0.886, respectively (all p < 0.05). The AUCs of ML models using random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM) were 0.871, 0.851, and 0.863, respectively. There were no difference among AUCs of conventional LR, RF, and SVM (all p > 0.05/6), while the AUC of MLP was lower than that of conventional LR (p = 0.0055).Conclusion: Hemodynamic parameters play an important role in the prediction performance of the models. ML methods cannot outperform conventional LR in prediction models for rupture status of UIAs integrating clinical, aneurysm morphological, and hemodynamic parameters.Key Points: • The addition of hemodynamic parameters can improve prediction performance for rupture status of unruptured intracranial aneurysms. • Machine learning algorithms cannot outperform conventional logistic regression in prediction models for rupture status integrating clinical, aneurysm morphological, and hemodynamic parameters. • Models integrating clinical, aneurysm morphological, and hemodynamic parameters may help choose the optimal management. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the features and trends of Radiology research in Mainland China through bibliometric analysis of the original articles published in Radiology and European Radiology (ER) between 2006 and 2015.Materials and Methods: We reviewed the original articles published in Radiology and ER between 2006 and 2015. The following information was abstracted: imaging subspecialty, imaging technique(s) used, research type, sample size, study design, statistical analysis, study results, funding declarations, international collaborations, number of authors, department and province of the first author. All variables were examined longitudinally over time.Results: Radiology research in Mainland China saw a substantial increase in original research articles published, especially in the last 5 years (P < 0.001). Within Mainland China's Radiology research, neuroradiology, vascular/interventional Radiology, and abdominal Radiology were the most productive fields; MR imaging was the most used modality, and a distinct geographic provenience was observed for articles published in Radiology and ER.Conclusion: Radiology research in Mainland China has seen substantial growth in the past 5 years with neuroradiology, vascular/interventional Radiology, and abdominal Radiology as the most productive fields. MR imaging is the most used modality. Article provenience shows a distinct geographical pattern.Key Points: • Radiology research in Mainland China saw a substantial increase. • Neuroradiology, vascular/interventional Radiology, and abdominal Radiology are the most productive fields. • MRI is the most used modality in Mainland China's Radiology research. • Guangdong, Shanghai, and Beijing are the most productive provinces. [ABSTRACT FROM AUTHOR]

Rapid economic growth in China has brought about great economic-social changes in rural areas, having considerable impact on the society in economy and environment. With a per capita possession of about 0.08 ha of cropland, Chinese farmers in rural areas adopt various ways in response to these changes in a bit to maintain their livelihood, wherein the agricultural system is facing one more options possible. To understand how rural communities have used different mechanisms to adapt to the economic and natural changes, we joined a survey in dry valleys of the Min upriver area under Maoxian county of western Sichuan province, southwestern China and visited the local people. Changes in the main crop cultivation have shown up an important means to keep up their household income. Farm households start seeking economic growth through diversified cultivating of cereal and economic crops in five lines, namely cereal, apple monoculture, apple and vegetables, plum and vegetables, mixed fruits and vegetables. These new lines mirror farmers' flexibility to cope with today's economic-social and climatic changes. The farming operation has changed all the more from a subsistence on grain to special agricultural products. Economic reforms in the early 1980s motivated the progress first in conversion of production from grain to fruits, and the desire to increase family income turned out to be an impetus for the subsequent events. At present, more farmers moving out of the rural areas, uneasy availability of labor force, increased opportunity cost of labors and their wages, increased farm size, and the urgent demand for the agricultural labor force, all these combine into the trend of the agricultural system of China on facing further economic-social reforms and reconstruction of the countryside across China. [ABSTRACT FROM AUTHOR]

Background: Lipid management based on cardiovascular risk level is the cornerstone of primary prevention of coronary artery disease (CAD), while the accuracy and adherence of traditional cardiovascular risk stratification have been questioned. Prevention strategies based on imaging screening for atherosclerotic plaques are found to be more objective and adherent in recent studies. This trial aims to investigate the role of coronary computed tomography angiography (CCTA) in guiding the primary prevention of CAD in a randomized controlled design., Methods: Approximately 3400 middle-aged asymptomatic community participants will be recruited and randomized in a 1:1 ratio to a traditional cardiovascular risk score-guided (usual care group) or CCTA-guided (CCTA group) strategy. Participants with cardiovascular disease, prior lipid-lowering therapy, CCTA contraindication, or serious diseases that affect life span will be excluded. The intervention strategy includes blood pressure, blood glucose, and lipid management and lifestyle modifications. Blood pressure and glucose targets and lifestyle modification recommendations keep the same in both strategies, while lipid management is personalized based on traditional risk level or CCTA results, respectively. The primary outcome is the proportion of participants taking lipid-lowering medication regularly at both 6 and 12 months. The secondary outcomes include the proportion of participants achieving low-density lipoprotein cholesterol lowering targets at 12 months, mean changes in lipid levels from baseline to 12 months, barriers to adherence, adverse reactions related to CCTA examination, and cardiovascular events., Discussion: The study is the first randomized clinical trial to examine the effectiveness of a CCTA-guided versus a traditional risk score-guided primary prevention strategy in an asymptomatic community-based population., Trial Registration: ClinicalTrials.gov NCT05725096. Registered on 2 February 2023., (© 2024. The Author(s).)

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: This study sought to determine whether machine learning (ML) can be used to better identify the risk factors and establish the prediction models for the prevalence and severity of coronary artery calcification (CAC) in nondialysis chronic kidney disease (CKD) patients and compare the performance of distinctive ML models with conventional logistic regression (LR) model., Materials and Methods: In all, 3701 Chinese nondialysis CKD patients undergoing noncontrast cardiac computed tomography (CT) scanning were enrolled from November 2013 to December 2017. CAC score derived from the cardiac CT was calculated with the calcium scoring software and was used to assess and stratify the prevalence and severity of CAC. Four ML models (LR, random forest, support vector machine, and k-nearest neighbor) and the corresponding feature ranks were conducted. The model that incorporated the independent predictors was shown as the receiver-operating characteristic (ROC) curve. Area under the curve (AUC) was used to present the prediction value. ML model performance was compared with the traditional LR model using pairwise comparisons of AUCs., Results: Of the 3701 patients, 943 (25.5%) patients had CAC. Of the 943 patients with CAC, 764 patients (20.6%) and 179 patients (4.8%) had an Agatston CAC score of 1 to 300 and ≥300, respectively. The primary cohort and the independent validation cohort comprised 2957 patients and 744 patients, respectively. For the prevalence of CAC, the AUCs of ML models were from 0.78 to 0.82 in the training data set and the internal validation cohort. For the severity of CAC, the AUCs of the 4 ML models were from 0.67 to 0.70 in the training data set and from 0.53 to 0.70 in the internal validation cohort. For the prevalence of CAC, the AUC was 0.80 (95% confidence interval [CI]: 0.77-0.83) for ML (LR) versus 0.80 (95% CI: 0.77-0.83) for the traditional LR model ( P =0.2533). For the severity of CAC, the AUC was 0.70 (95% CI: 0.63-0.77) for ML (LR) versus 0.70 (95% CI: 0.63-0.77) for traditional LR model ( P =0.982)., Conclusions: This study constructed prediction models for the presence and severity of CAC based on Agatston scores derived from noncontrast cardiac CT scanning in nondialysis CKD patients using ML, and showed ML LR had the best performance., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The G allele in retinoid-related orphan receptor alpha (RORA, rs8042149) gene is associated with post-traumatic stress disorder (PTSD) diagnosis and more severe symptoms, reported in the first genome-wide association study of PTSD and subsequent replication studies. Although recent MRI studies identified brain structural deficits in RORA rs8042149 risk G allele carriers, the neural mechanism underlying RORA-related brain structural changes in PTSD remains poorly understood., Methods: This study included 227 Han Chinese adults who lost their only child. Cortical thickness and subcortical volume were extracted using FreeSurfer, and PTSD severity was assessed using the Clinician-Administered PTSD Scale. Hierarchical linear regression was used to assess the interaction effect between RORA genotypes (T/T, G/T, and G/G) and PTSD severity on cortical and subcortical structures., Results: Significant genotype × PTSD symptom severity interaction effects were found for bilateral transverse temporal gyrus thickness. For individuals with the homozygous T/T genotype, current PTSD symptom severity was positively associated with bilateral transverse temporal gyrus thickness. For individuals with heterozygous G/T genotype, current PTSD symptom severity was negatively associated with the left transverse temporal gyrus thickness. No significant main or interaction effects were found in any subcortical regions., Limitation: Cross-sectional design of this study., Conclusion: These findings suggest that the non-risk T/T genotype - but not the risk G allele carriers - has a potentially protective or compensatory role on temporal gyrus thickness in adults who lost their only child. These results highlight the moderation effect of RORA polymorphism on the relationship between PTSD symptom severity and cortical structural changes., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Objective: To investigate the effect of coronary calcification morphology and severity on the diagnostic performance of machine learning (ML)-based coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) with FFR as a reference standard., Methods: A total of 442 patients (61.2 ± 9.1 years, 70% men) with 544 vessels who underwent CCTA, ML-based CT-FFR, and invasive FFR from China multicenter CT-FFR study were enrolled. The effect of calcification arc, calcification remodeling index (CRI), and Agatston score (AS) on the diagnostic performance of CT-FFR was investigated. CT-FFR ≤ 0.80 and lumen reduction ≥ 50% determined by CCTA were identified as vessel-specific ischemia with invasive FFR as a reference standard., Results: Compared with invasive FFR, ML-based CT-FFR yielded an overall sensitivity of 0.84, specificity of 0.94, and accuracy of 0.90 in a total of 344 calcification lesions. There was no statistical difference in diagnostic accuracy, sensitivity, or specificity of CT-FFR across different calcification arc, CRI, or AS levels. CT-FFR exhibited improved discrimination of ischemia compared with CCTA alone in lesions with mild-to-moderate calcification (AUC, 0.89 vs. 0.69, p < 0.001) and lesions with CRI ≥ 1 (AUC, 0.89 vs. 0.71, p < 0.001). The diagnostic accuracy and specificity of CT-FFR were higher than CCTA alone in patients and vessels with mid (100 to 299) or high (≥ 300) AS., Conclusion: Coronary calcification morphology and severity did not influence diagnostic performance of CT-FFR in ischemia detection, and CT-FFR showed marked improved discrimination of ischemia compared with CCTA alone in the setting of calcification., Key Points: • CT-FFR provides superior diagnostic performance than CCTA alone regardless of coronary calcification. • No significant differences in the diagnostic performance of CT-FFR were observed in coronary arteries with different coronary calcification arcs and calcified remodeling indexes. • No significant differences in the diagnostic accuracy of CT-FFR were observed in coronary arteries with different coronary calcification score levels.

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.

In December 2019, an outbreak of severe acute respiratory syndrome coronavirus 2 infection occurred in Wuhan, Hubei Province, China, and spread across China and beyond. On February 12, 2020, the World Health Organization officially named the disease caused by the novel coronavirus as coronavirus disease 2019 (COVID-19). Because most patients infected with COVID-19 had pneumonia and characteristic CT imaging patterns, radiologic examinations have become vital in early diagnosis and the assessment of disease course. To date, CT findings have been recommended as major evidence for clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the etiology, epidemiology, and clinical symptoms of COVID-19 while highlighting the role of chest CT in prevention and disease control., (© RSNA, 2020.)

Coronavirus disease 2019 (COVID-19) is a new infectious disease rapidly spreading around the world, raising global public health concerns. Radiological examinations play a crucial role in the early diagnosis and follow-up of COVID-19. Cross infection among patients and radiographers can occur in radiology departments due to the close and frequent contact of radiographers with confirmed or potentially infected patients in a relatively confined room during radiological workflow. This article outlines our experience in the emergency management procedure and infection control of the radiology department during the COVID-19 outbreak., (Copyright © 2020 The Korean Society of Radiology.)

The ongoing outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly throughout China and other countries, and has been characterized as a pandemic. With the strict prevention and control measures implemented by the Chinese government, the spread of the epidemic in China has come under preliminary control by the end of February, 2020, whereas the situation of some countries outside China is not optimistic and raises great public concern. In fighting COVID-19, radiologic examinations played a critical role in the early diagnosis of COVID-19, and could also help assess disease course and severity. There is an urgent need to share useful experience and effective measures internationally. This article outlines the collaborative actions and efforts by the Chinese radiology field and the situation of front-line health care workers in radiology departments to present the world with experiences and examples of Chinese radiology during the COVID-19 outbreak.

Background: The risk factors for adverse events of Coronavirus Disease-19 (COVID-19) have not been well described. We aimed to explore the predictive value of clinical, laboratory and CT imaging characteristics on admission for short-term outcomes of COVID-19 patients. Methods: This multicenter, retrospective, observation study enrolled 703 laboratory-confirmed COVID-19 patients admitted to 16 tertiary hospitals from 8 provinces in China between January 10, 2020 and March 13, 2020. Demographic, clinical, laboratory data, CT imaging findings on admission and clinical outcomes were collected and compared. The primary endpoint was in-hospital death, the secondary endpoints were composite clinical adverse outcomes including in-hospital death, admission to intensive care unit (ICU) and requiring invasive mechanical ventilation support (IMV). Multivariable Cox regression, Kaplan-Meier plots and log-rank test were used to explore risk factors related to in-hospital death and in-hospital adverse outcomes. Results: Of 703 patients, 55 (8%) developed adverse outcomes (including 33 deceased), 648 (92%) discharged without any adverse outcome. Multivariable regression analysis showed risk factors associated with in-hospital death included ≥ 2 comorbidities (hazard ratio [HR], 6.734; 95% CI; 3.239-14.003, p < 0.001), leukocytosis (HR, 9.639; 95% CI, 4.572-20.321, p < 0.001), lymphopenia (HR, 4.579; 95% CI, 1.334-15.715, p = 0.016) and CT severity score > 14 (HR, 2.915; 95% CI, 1.376-6.177, p = 0.005) on admission, while older age (HR, 2.231; 95% CI, 1.124-4.427, p = 0.022), ≥ 2 comorbidities (HR, 4.778; 95% CI; 2.451-9.315, p < 0.001), leukocytosis (HR, 6.349; 95% CI; 3.330-12.108, p < 0.001), lymphopenia (HR, 3.014; 95% CI; 1.356-6.697, p = 0.007) and CT severity score > 14 (HR, 1.946; 95% CI; 1.095-3.459, p = 0.023) were associated with increased odds of composite adverse outcomes. Conclusion: The risk factors of older age, multiple comorbidities, leukocytosis, lymphopenia and higher CT severity score could help clinicians identify patients with potential adverse events., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Objectives: The aim of this study was to validate the feasibility of a novel structural and computational fluid dynamics-based fractional flow reserve (FFR) algorithm for coronary computed tomography angiography (CTA), using alternative boundary conditions to detect lesion-specific ischemia., Background: A new model of computed tomographic (CT) FFR relying on boundary conditions derived from structural deformation of the coronary lumen and aorta with transluminal attenuation gradient and assumptions regarding microvascular resistance has been developed, but its accuracy has not yet been validated., Methods: A total of 338 consecutive patients with 422 vessels from 9 Chinese medical centers undergoing CTA and invasive FFR were retrospectively analyzed. CT FFR values were obtained on a novel on-site computational fluid dynamics-based CT FFR (uCT-FFR [version 1.5, United-Imaging Healthcare, Shanghai, China]). Performance characteristics of uCT-FFR and CTA in detecting lesion-specific ischemia in all lesions, intermediate lesions (luminal stenosis 30% to 70%), and "gray zone" lesions (FFR 0.75 to 0.80) were calculated with invasive FFR as the reference standard. The effect of coronary calcification on uCT-FFR measurements was also assessed., Results: Per vessel sensitivities, specificities, and accuracies of 0.89, 0.91, and 0.91 with uCT-FFR, 0.92, 0.34, and 0.55 with CTA, and 0.94, 0.37, and 0.58 with invasive coronary angiography, respectively, were found. There was higher specificity, accuracy, and AUC for uCT-FFR compared with CTA and qualitative invasive coronary angiography in all lesions, including intermediate lesions (p < 0.001 for all). No significant difference in diagnostic accuracy was observed in the "gray zone" range versus the other 2 lesion groups (FFR ≤0.75 and >0.80; p = 0.397) and in patients with "gray zone" versus FFR ≤0.75 (p = 0.633) and versus FFR >0.80 (p = 0.364), respectively. No significant difference in the diagnostic performance of uCT-FFR was found between patients with calcium scores ≥400 and <400 (p = 0.393)., Conclusions: This novel computational fluid dynamics-based CT FFR approach demonstrates good performance in detecting lesion-specific ischemia. Additionally, it outperforms CTA and qualitative invasive coronary angiography, most notably in intermediate lesions, and may potentially have diagnostic power in gray zone and highly calcified lesions., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18-30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.