Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD.

Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a novel artificial intelligence-based fully-automated, on-site CT-FFR technology, which combines the automated coronary plaque segmentation and luminal extraction model with reduced order 3 dimentional (3D) computational fluid dynamics. A total of 463 consecutive patients with 600 vessels from the updated China CT-FFR study in Cohort 1 undergoing both CCTA and invasive fractional flow reserve (FFR) within 90 d were collected for diagnostic performance evaluation. For Cohort 2, a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed. In Cohort 3, the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated. The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level. Compared with the manually dependent CT-FFR techniques, the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1. This CT-FFR technique has a highly successful (> 99%) calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain. Thus, the novel artificial intelligence-based fully automated, on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.

Neurological prognosis in surgically treated acute aortic dissection with brain computed tomography perfusion.

OBJECTIVES: The aim of this study was to explore the prognostic value of brain computed tomography perfusion (CTP) for postoperative new stroke in acute type A aortic dissection (ATAAD) patients. METHODS: Patients with ATAAD and suspected cerebral malperfusion who underwent brain CTP and surgical repair were retrospectively analysed. Brain perfusion was quantified mainly with the averaged cerebral blood flow. Significant clinical and imaging findings were identified through univariable and multivariable regression analysis. Furthermore, the added prognostic benefit of perfusion parameters was confirmed with the receiver operating characteristic curves in the entire cohort and subgroup analysis. RESULTS: The incidence of postoperative new stroke was 30.8% (44/143). The independent adjusted predictors of postoperative new stroke included an impaired averaged cerebral blood flow (ml/100 ml/min) (odds ratio: 0.889; P < 0.001), severe stenosis (odds ratio: 5.218; P = 0.011) or occlusion (odds ratio: 14.697; P = 0.048) of the true lumen in common carotid artery (CCA), hypotension on admission (odds ratio: 9.644; P = 0.016) and a longer surgery time (odds ratio: 1.593; P = 0.021). The area under the receiver operating characteristic curves significantly improved after adding perfusion parameters to clinical and computed tomography angiography characteristics (P = 0.048). This benefit was more pronounced in patients with severe stenosis or occlusion in CCA true lumen (P = 0.004). CONCLUSIONS: Brain CTP could be a useful prognostic tool for surgically treated ATAAD patients and especially beneficial in patients with severe stenosis or occlusion of the CCA true lumen.

The restoration ability of a short nap after sleep deprivation on the brain cognitive function: A dynamic functional connectivity analysis.

AIMS: The brain function impairment induced by sleep deprivation (SD) is temporary and can be fully reversed with sufficient sleep. However, in many cases, long-duration recovery sleep is not feasible. Thus, this study aimed to investigate whether a short nap after SD is sufficient to restore brain function. METHODS: The data of 38 subjects, including resting state functional magnetic resonance imaging data collected at three timepoints (before SD, after 30 h of SD, and after a short nap following SD) and psychomotor vigilance task (PVT) data, were collected. Dynamic functional connectivity (DFC) analysis was used to evaluate changes in brain states among three timepoints, and four DFC states were distinguished across the three timepoints. RESULTS: Before SD, state 2 (a resting-like FC matrix) was dominant (48.26%). However, after 30 h SD, the proportion of state 2 dramatically decreased, and state 3 (still resting-like, but FCs were weakened) became dominant (40.92%). The increased proportion of state 3 positively correlated with a larger PVT "lapse" time. After a nap, the proportions of states 2 and 3 significantly increased and decreased, respectively, and the change in proportion of state 2 negatively correlated with the change in PVT "lapse" time. CONCLUSIONS: Taken together, the results indicated that, after a nap, the cognitive function impairment caused by SD may be reversed to some extent. Additionally, DFC differed among timepoints, which was also associated with the extent of cognitive function impairment after SD (state 3) and the extent of recovery therefrom after a nap (state 2).

White matter structural topologic efficiency predicts individual resistance to sleep deprivation.

BACKGROUND: Sleep deprivation (SD) is commonplace in modern society and there are large individual differences in the vulnerability to SD. We aim to identify the structural network differences based on diffusion tensor imaging (DTI) that contribute to the individual different vulnerability to SD. METHODS: The number of psychomotor vigilance task (PVT) lapses was used to classify 49 healthy subjects on the basis of whether they were vulnerable or resistant to SD. DTI and graph theory approaches were used to investigate the topologic organization differences of the brain structural connectome between SD-vulnerable and -resistant individuals. We measured the level of global efficiency and clustering in rich club and non-rich club organizations. RESULTS: We demonstrated that participants vulnerable to SD had less global efficiency, network strength, and local efficiency but longer shortest path length compared with participants resistant to SD. Lower efficiency was mainly distributed in the right insula, bilateral thalamus, bilateral frontal, temporal, and temporal lobes. Furthermore, a disrupted subnetwork was observed that consisted of widespread connections. Moreover, the vulnerable group showed significantly decreased strength of the rich club compared with the resistant group. The strength of rich club connectivity was found to be correlated negatively with PVT performance (r = -0.395, p = 0.005). We further tested the reliability of the results. CONCLUSION: The findings revealed that individual differences in resistance to SD are related to disrupted topologic efficiency connectome pattern, and our study may provide potential connectome-based biomarkers for the early detection of the vulnerable degree to SD.

Prognostic Value of Non-alcoholic Fatty Liver Disease and RCA Pericoronary Adipose Tissue CT Attenuation in Patients with Acute Chest Pain.

RATIONALE AND OBJECTIVES: Pericoronary adipose tissue (PCAT) CT attenuation of right coronary artery (RCA) and non-alcoholic fatty liver disease (NAFLD) have prognostic value for major adverse cardiovascular events (MACE) in patients with coronary artery disease. However, the superior prognostic value between RCA PCAT CT attenuation and NAFLD remains unclear in patients with acute chest pain. This study is to evaluate the prognostic value of NAFLD for MACE, and further assess the incremental prognostic value of NAFLD over PCAT CT attenuation. MATERIALS AND METHODS: Between January 2011 and December 2021, all consecutive emergency patients with acute chest pain referred for coronary CT angiography (CCTA) were retrospectively enrolled. MACE included unstable angina requiring hospitalization, coronary revascularization, non-fatal myocardial infarction, and all-cause death. Patients' baseline and CCTA characteristics, RCA PCAT CT attenuation, and the presence of NAFLD were used to evaluate risk factors of MACE using multivariable Cox regression analysis. The prognostic value of NAFLD compared to RCA PCAT CT attenuation was analyzed. RESULTS: A total of 514 patients were enrolled (mean age, 58.36 ± 13.05 years; 310 men). During a median follow-up of 31 months, 60 patients (11.67%) experienced MACE. NAFLD (HR = 2.599, 95% CI: 1.207, 5.598, P = 0.015) and RCA PCAT CT attenuation (HR = 1.026, 95% CI: 1.001, 1.051, P = 0.038) were independent predictors of MACE. The global Chi-square analysis showed that NAFLD improved the risk of MACE more than that using clinical risk factors and CCTA metrics (59.51 vs 54.44, P = 0.024) or combined with RCA PCAT CT attenuation (63.75 vs 59.51, P = 0.040). CONCLUSION: NAFLD and RCA PCAT CT attenuation were predictors of MACE. NAFLD had an incremental prognostic value beyond RCA PCAT CT attenuation for MACE in patients with acute chest pain. Adding CT-FFR into the risk prediction of patients with acute chest pain is worth considering.

Cognitive impairment after sleep deprivation: The role of precuneus related connectivity on the intra-individual variability changes.

OBJECTIVE: Intra-individual variability (IIV) in cognitive performance is thought to reflect the efficiency with which attentional resources are allocated in different circumstances requiring cognitive control. IIV in cognitive performance is associated with the strength of the negative correlation between task-positive network and default mode network (DMN) activity. In this study, we investigated the impact of sleep deprivation (SD) on functional connectivity (FC) between the DMN and psychomotor vigilance task-related network (PVT-RN), and its relationship with IIV in cognitive performance. METHODS: Two analyses, network-level independent component analysis (NL-ICA) and region-level (RL)-ICA, were employed to compare the coefficient of variation (CV) of the PVT between normal sleep and SD conditions across 67 healthy participants. RESULTS: After SD, in NL-ICA, the FC between the PVT-RN and DMN was positively correlated with the CV of the PVT, as well as the changes therein, compared with normal sleep. Using a mask derived from the DMN and PVT-RN, the RL-ICA revealed that 12 edges/connections between DMN and PVT independent components were associated with the CV of the PVT, with nine of these connections involving the precuneus. CONCLUSIONS: These findings suggest that the precuneus may play a crucial role in the interactions of various brain functions during the PVT, with the connections between the precuneus and frontoparietal and somatosensory networks being significantly altered after SD. Moreover, following SD, weakened negative FC between the precuneus and bilateral inferior parietal lobule may disrupt the balance between cognitive and executive control functions, leading to a decline in cognitive performance.

Association of Objective Sleep Characteristics and Incident Angina Pectoris: A Longitudinal Analysis from the Sleep Heart Health Study.

Purpose: While prior research has highlighted a significant association between sleep characteristics and angina pectoris (AP) incidence, the link between sleep efficiency (SE) and angina remains unexplored. This study seeks to elucidate the relationship between AP and objectively quantified SE. Patients and Methods: We examined a cohort of 2990 participants (1320 males and 1670 females; mean age 63.69 ± 13.2 years) from the Sleep Heart Health Study. The main exposure variable was SE, as determined by baseline home polysomnography, while the primary outcome was the first incidence of angina pectoris (AP) during the period between the baseline polysomnography and the end of follow-up. A multivariate Cox regression model was utilized, controlling for factors such as age, gender, BMI, smoking and alcohol consumption habits, diabetes, hypertension, sleep duration, triglycerides, cholesterol, high-density lipoprotein, apnea-hypopnea index, nocturnal oxygen saturation, to analyze the relationship between SE and AP. Results: During an average follow-up of 11 years, 284 patients developed AP. The unadjusted Kaplan-Meier analysis identified the 2nd quartile of SE as having the lowest AP risk. The multivariate Cox proportional hazards model demonstrated a higher risk of AP in quartile 1 (HR, 1.679; 95% CI, 1.109-2.542; P <0.014) and quartile 3 (HR, 1.503; 95% CI, 1.037-2.179; P <0.031), compared to quartile 2 of SE. Upon stratified analysis, this relationship was particularly pronounced in hypertensive individuals. Conclusion: Our results highlight the critical role of optimal sleep efficiency in mitigating the risk of angina pectoris, especially among hypertensive individuals.

Treatment of Severe Pulmonary Regurgitation in Enlarged Native Right Ventricular Outflow Tracts: Transcatheter Pulmonary Valve Replacement with Three-Dimensional Printing Guidance.

BACKGROUND: Severe pulmonary regurgitation (PR) often occurs following a transannular patch repair of tetralogy of Fallot, resulting in an enlarged native right ventricular outflow tract (nRVOT) with varying shapes. METHODS: We conducted a single-center study with eight patients having severe PR and enlarged nRVOT (diameters ≥ 29 mm). Transcatheter pulmonary valve replacement (TPVR) was performed using the self-expanding PT-Valve. Preoperative evaluation included echocardiography, computed tomography, and magnetic resonance imaging. A 3D-printed model of the nRVOT was used for preoperative assessment. Follow-up data were collected in 1-year follow-up. RESULTS: PT-Valve was successfully implanted in all patients, resulting in immediate improvement of severe PR. Pulmonary artery diastolic pressure increased significantly (p < 0.001). No deaths or coronary compression occurred during the procedure. Over a 1-year follow-up, no stent displacement or fracture occurred. Only two patients had trace paravalvular leaks. Magnetic resonance imaging revealed a reversal of right ventricular remodeling, with a significant reduction in right ventricular end-diastolic volume index (p < 0.001) and improved right ventricular ejection fraction (p < 0.001). All patients achieved primary endpoints. CONCLUSION: 3D printing-guided PT-Valve implantation in enlarged nRVOT for severe PR is safe and effective, expanding TPVR indications and offering potential treatment for a broader patient population.

Prognostic value of coronary CT angiography and CT myocardial perfusion imaging among patients with and without Diabetes.

OBJECTIVES: Whether stress CT myocardial perfusion imaging (CT-MPI) improves risk assessment in patients with diabetes mellitus (DM) remains unexplored. We aimed to evaluate the prognostic value of coronary CT angiography (CCTA) and stress CT-MPI in suspected coronary artery disease (CAD) patients with and without DM. METHODS: A total of 334 patients with suspected CAD who underwent CCTA and stress CT-MPI from May 2020 to July 2021 were retrospectively analyzed. The endpoint was major adverse cardiovascular events (MACEs). Multivariable Cox regression analysis was used to evaluate the risk factors for MACEs, including clinical risk factors, CCTA characteristics and CT-MPI characteristics. RESULTS: After a median follow-up of 21 months,15 patients of the DM group and 16 patients of the non-DM group experienced MACEs. Multivariate Cox stepwise regression analysis showed that abnormal perfusion myocardial segments ratio was associated with MACEs after adjusting for clinical risk factors and CCTA characteristics in all patients (HR:1.023, p < 0.001), DM group (HR:1.024, p = 0.008) and non-DM group (HR:1.028, p = 0.003). By adding CT-MPI characteristics to CCTA characteristics and clinical risk factors, the global chi-square for predicting MACEs increased from 62.24 to 78.84 in all patients (p < 0.001), from 19.18 to 27.30 in DM group (p = 0.004) and from 39.51 to 48.65 in non-DM group (p = 0.003); the increment of C-index in all patients, DM group and non-DM group were 0.018, 0.054 and 0.019, respectively. CONCLUSION: In all patients and those with and without DM, CT-MPI has incremental prognostic value over clinical risk factors alone or combined with CCTA characteristics in predicting MACEs.

Lack of Incremental Prognostic Value of Pericoronary Adipose Tissue Computed Tomography Attenuation Beyond Coronary Artery Disease Reporting and Data System for Major Adverse Cardiovascular Events in Patients With Acute Chest Pain.

BACKGROUND: Pericoronary adipose tissue (PCAT) and Coronary Artery Disease Reporting and Data System (CAD-RADS) category had prognostic values for major adverse cardiovascular events (MACEs). However, little is known about the difference between CAD-RADS and PCAT computed tomography (CT) attenuation for predicting MACEs. This study was to compare the prognostic value of PCAT and CAD-RADS for MACEs in patients with acute chest pain. METHODS: Between January 2010 and December 2021, all consecutive emergency patients with acute chest pain referred for coronary computed tomography angiography were enrolled in this retrospective study. MACEs included unstable angina requiring hospitalization, coronary revascularization, nonfatal myocardial infarction, and all-cause death. Patients' clinical characteristics, CAD-RADS, and PCAT CT attenuation were used to evaluate risk factors of MACEs using multivariable Cox regression analysis. RESULTS: A total of 1313 patients were evaluated (mean age, 57.13±12.57 years; 782 men). During a median follow-up of 38 months, 142 of the 1313 patients (10.81%) experienced MACEs. Multivariable Cox regression analysis showed that CAD-RADS categories 2, 3, 4, 5 (hazard ratio range, 2.286-8.325; all P<0.005) and right coronary artery PCAT CT attenuation (hazard ratio, 1.033; P=0.006) were independent predictors of MACEs after adjusting for clinical risk factors. The C statistics revealed that CAD-RADS improved risk stratification compared with PCAT CT alone (C-index, 0.760 versus 0.712; P=0.036). However, the benefit of right coronary artery PCAT CT attenuation combined with CAD-RADS was not significant compared with CAD-RADS alone (0.777 versus 0.760; P=0.129). CONCLUSIONS: Right coronary artery PCAT CT attenuation and CAD-RADS were independent predictors of MACEs. However, no incremental prognostic value of right coronary artery PCAT CT attenuation beyond CAD-RADS was detected for MACEs in patients with acute chest pain.

Risk prediction of preoperative acute ischemic stroke in acute type A aortic dissection.

OBJECTIVES: To predict preoperative acute ischemic stroke (AIS) in acute type A aortic dissection (ATAAD). METHODS: In this multi-center retrospective study, 508 consecutive patients diagnosed as ATAAD between April 2020 and March 2021 were considered for inclusion. The patients were divided into a development cohort and two validation cohorts based on time periods and centers. Clinical data and imaging findings obtained were analyzed. Univariable and multivariable logistic regression analyses were performed to identify predictors associated with preoperative AIS. The performance of resulting nomogram was evaluated in discrimination and calibration on all cohorts. RESULTS: A total of 224 patients were in the development cohort, 94 in the temporal validation cohort, and 118 in the geographical validation cohort. Six predictors were identified: age, syncope, D-dimer, moderate to severe aortic valve insufficiency, diameter ratio of true lumen in ascending aorta < 0.33, and common carotid artery dissection. The nomogram established showed good discrimination (area under the receiver operating characteristic curve [AUC], 0.803; 95% CI: 0.742, 0.864) and calibration (Hosmer-Lemeshow test p = 0.300) in the development cohort. External validation showed good discrimination and calibration abilities in both temporal (AUC, 0.778; 95% CI: 0.671, 0.885; Hosmer-Lemeshow test p = 0.161) and geographical cohort (AUC, 0.806; 95% CI: 0.717, 0.895; Hosmer-Lemeshow test p = 0.100). CONCLUSIONS: A nomogram, based on simple imaging and clinical variables collected on admission, showed good discrimination and calibration abilities in predicting preoperative AIS for ATAAD patients. KEY POINTS: • A nomogram based on simple imaging and clinical findings may predict preoperative acute ischemic stroke in patients with acute type A aortic dissection in emergencies. • The nomogram showed good discrimination and calibration abilities in validation cohorts.

Nonalcoholic fatty liver disease is associated with myocardial ischemia by CT myocardial perfusion imaging, independent of clinical and coronary CT angiography characteristics.

OBJECTIVE: The aim of this study was to evaluate whether patients with nonalcoholic fatty liver disease (NAFLD) have more myocardial malperfusion on CT myocardial perfusion imaging (CT-MPI), as well as to further assess if NAFLD is a predictor of myocardial ischemia independently. METHODS: A total of 310 consecutive patients were included for analysis. All patients were divided into two groups according to the presence or absence of NAFLD, which was diagnosed by noncontrast cardiac CT partially covered liver and spleen. Clinical characteristics as well as imaging features including coronary artery calcium score, CCTA, and CT-MPI findings were analyzed. Univariable and multivariable logistic regression analyses were used to find out the relationship between NAFLD and myocardial ischemia. RESULTS: NAFLD (unadjusted hazard ratio [HR]: 2.4, 95% confidence interval [CI]: 1.2 to 4.4, p = 0.008), male (HR: 2.6, 95% CI: 1.5 to 4.5, p = 0.001), obstructive CAD (HR: 2.3, 95% CI: 1.3 to 4.2, p = 0.004), and FAI ≥ -70.1 HU (HR: 3.1, 95% CI: 1.8 to 5.5, p < 0.001) were associated with myocardial ischemia in univariable analysis. After adjusting for traditional CAD risk factors and CT characteristics in the multivariable regression analysis, NAFLD (HR: 2.3, 95% CI: 1.2 to 4.4, p = 0.016) was an independent predictor of myocardial ischemia. CONCLUSION: Our data suggest that myocardial ischemia was more prevalent in patients with NAFLD, and NAFLD is a predictor of myocardial ischemia independent of traditional cardiovascular risk factors and CCTA characteristics. KEY POINTS: • NAFLD patients had higher calcium score, incidence of obstructive coronary artery disease, grade of CAD-RADS, quantitative plaque characteristics, and incidence of fat attenuation index ≥ -70.1 HU. • NAFLD patients had a higher incidence of myocardial ischemia, myocardial hypoperfusion, and hypoperfusion myocardial segments ratio. • NAFLD was a predictor of myocardial ischemia, independent of traditional cardiovascular risk factors, and CCTA characteristics.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology.

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.

Predictive Value of CT-Based Radiomics in Distinguishing Renal Angiomyolipomas with Minimal Fat from Other Renal Tumors.

Objectives: This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods: This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results: Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions: Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors.

Improved interobserver agreement on nodule type and Lung-RADS classification of subsolid nodules using computer-aided solid component measurement.

PURPOSE: The Lung CT Screening Reporting and Data System (Lung-RADS) classification of subsolid nodules (SSNs) can be challenging due to limited interobserver agreement in determining the type and size of the nodule. Our study aimed to assess the effect of a computer-aided method on the interobserver agreement of Lung-RADS classification for SSNs. MATERIALS AND METHODS: This study consisted of 156 SSNs in 121 patients who underwent initial CT screening for lung cancer. Three independent readers determined the nodule type and measured the size of the entire nodule as well as the solid component, first without and then assisted by a semi-automated computer-aided tool. They assigned to each nodule the corresponding Lung-RADS 1.1 category. Agreement in size measurements was assessed by intraclass correlation coefficient (ICC) and Bland-Altman indexes, while agreement in nodule type and Lung-RADS was determined using Fleiss kappa statistics. The relationship between final diagnosis of the nodules and Lung-RADS classifications was also evaluated. RESULTS: Among the 156 nodules, manual size measurement reached an ICC of 0.994, and 48 nodules contained solid component measured by all the three readers both manually and semi-automatically. ICCs for the solid component measurement were 0.952, 0.997 and 0.996 for manual diameter, semi- automated diameter and volume measurement, respectively. Bias and 95% limits of agreement for average diameter of solid component were smaller with semi-automated measurements than with manual measurements. Kappa values of semi-automated assessment for nodule type (0.974) and Lung-RADS classification (0.958 for diameter and 0.952 for volume) were higher than with the manual measurements (0.783 for nodule type and 0.652 for Lung-RADS classification). Compared to manual work, the semi-automated assessment identified more 4B nodules among the 26 pathologically confirmed invasive adenocarcinomas (IACs). CONCLUSION: Semi-automated assessment could improve the interobserver agreement of nodule type and Lung-RADS classification for SSNs, and be inclined to classify SSNs corresponding to pathologically confirmed IACs into higher risk categories.

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data.

Background: Large individual differences exist in sleep deprivation (SD) induced sustained attention deterioration. Several brain imaging studies have suggested that the activities within frontal-parietal network, cortico-thalamic connections, and inter-hemispheric connectivity might underlie the neural correlates of vulnerability/resistance to SD. However, those traditional approaches are based on average estimates of differences at the group level. Currently, a neuroimaging marker that can reliably predict this vulnerability at the individual level is lacking. Methods: Efficient transfer of information relies on the integrity of white matter (WM) tracts in the human brain, we therefore applied machine learning approach to investigate whether the WM diffusion metrics can predict vulnerability to SD. Forty-nine participants completed the psychomotor vigilance task (PVT) both after resting wakefulness (RW) and after 24 h of sleep deprivation (SD). The number of PVT lapse (reaction time > 500 ms) was calculated for both RW condition and SD condition and participants were categorized as vulnerable (24 participants) or resistant (25 participants) to SD according to the change in the number of PVT lapses between the two conditions. Diffusion tensor imaging were acquired to extract four multitype WM features at a regional level: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) learning approach using leave-one-out cross-validation (LOOCV) was performed to assess the discriminative power of WM features in SD-vulnerable and SD-resistant participants. Results: LSVM analysis achieved a correct classification rate of 83.67% (sensitivity: 87.50%; specificity: 80.00%; and area under the receiver operating characteristic curve: 0.85) for differentiating SD-vulnerable from SD-resistant participants. WM fiber tracts that contributed most to the classification model were primarily commissural pathways (superior longitudinal fasciculus), projection pathways (posterior corona radiata, anterior limb of internal capsule) and association pathways (body and genu of corpus callosum). Furthermore, we found a significantly negative correlation between changes in PVT lapses and the LSVM decision value. Conclusion: These findings suggest that WM fibers connecting (1) regions within frontal-parietal attention network, (2) the thalamus to the prefrontal cortex, and (3) the left and right hemispheres contributed the most to classification accuracy.

Sleep Stage Classification Based on Multi-Centers: Comparison Between Different Ages, Mental Health Conditions and Acquisition Devices.

Purpose: To investigate the general sleep stage classification performance of deep learning networks, three datasets, across different age groups, mental health conditions, and acquisition devices, comprising adults (SHHS) and children without mental health conditions (CCSHS), and subjects with mental health conditions (XJ), were included in this study. Methods: A long short-term memory (LSTM) network was used to evaluate the effect of different ages, mental health conditions, and acquisition devices on the sleep stage classification performance and the general performance. Results: Results showed that the age and different mental health conditions may affect the sleep stage classification performance of the network. The same acquisition device using different parameters may not have an obvious effect on the classification performance. When using a single dataset and two datasets for training, the network performed better only on the training dataset. When training was conducted with three datasets, the network performed well for all datasets with a Cohen's Kappa of 0.8192 and 0.8472 for the SHHS and CCSHS, respectively, but performed relatively worse (0.6491) for the XJ, which indicated the complexity effect of different mental health conditions on the sleep stage classification task. Moreover, the performance of the network trained using three datasets was similar for each dataset to that of the network trained using a single dataset and tested on the same dataset. Conclusion: These results suggested that when more datasets across different age groups, mental health conditions, and acquisition devices (ie, more datasets with different feature distributions for each sleep stage) are used for training, the general performance of a deep learning network will be superior for sleep stage classification tasks with varied conditions.

Influence of Different Segmentations on the Diagnostic Performance of Pericoronary Adipose Tissue.

Objective: To investigate the influence of different segmentations on the diagnostic performance of pericoronary adipose tissue (PCAT) CT attenuation and radiomics features for the prediction of ischemic coronary artery stenosis. Methods: From June 2016 to December 2018, 108 patients with 135 vessels were retrospectively analyzed in the present study. Vessel-based PCAT was segmented along the 40 mm-long proximal segments of three major epicardial coronary arteries, while lesion-based PCAT was defined around coronary lesions. CT attenuation and radiomics features derived from two segmentations were calculated and extracted. The diagnostic performance of PCAT CT attenuation or radiomics models in predicting ischemic coronary stenosis were also compared between vessel-based and lesion-based segmentations. Results: The mean PCAT CT attenuation was -75.7 ± 9.1 HU and -76.1 ± 8.1 HU (p = 0.395) for lesion-based and vessel-based segmentations, respectively. A strong correlation was found between vessel-based and lesion-based PCAT CT attenuation for all cohort and subgroup analyses (all p < 0.01). A good agreement for all cohort and subgroup analyses was also detected between two segmentations. The diagnostic performance was comparable between vessel-based and lesion based PCAT CT attenuation in predicting ischemic stenosis. The radiomics features of PCAT based on vessel or lesion segmentation can both adequately identify the ischemic stenosis. However, no significant difference was detected between the two segmentations. Conclusions: The quantitative evaluation of PCAT can be reliably measured both from vessel-based and lesion-based segmentation. Furthermore, the radiomics analysis of PCAT may potentially help predict hemodynamically significant coronary artery stenosis.

Role of contrast-enhanced ultrasonography in MR-guided focused ultrasound ablation on uterus fibroids: lesion selection and assessment of ablative effects.

OBJECTIVES: To determine whether contrast-enhanced ultrasonography (CEUS) can be used for selecting lesions and assessing the ablative effects of MRgFUS ablation on uterus fibroids, compared with MR imaging. METHODS: This retrospective study was approved by the institutional review board of our hospital. From April 2018 to November 2019, a total of 44 symptomatic fibroids in 38 patients who underwent MRgFUS ablation were included. The association between pre-ablation characteristics on CEUS/MR imaging and the non-perfusion volume (NPV) after ablation was analyzed using multivariable linear regression analysis. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve values was compared between the CEUS and MR imaging regression models. NPV after ablation was compared between CEUS and enhanced MR imaging. RESULTS: On CEUS, entangled branch vessels, fast-in, and fast-out patterns were significantly associated with NPV, with an AUC of 0.95 (95% CI; 0.88, 1.00). On MR imaging, hyper-intensity on T2-weighted images (T2WI), hyper-intense ring-like signal on T2WI images, and hyper-enhancement on contrast-enhanced T1WI images were correlated with NPV, with an AUC of 0.86 (95% CI; 0.70, 1.00). After ablation, no differences in NPV were noted between contrast-enhanced T1WI (84.13 ± 75.42 cm3) and CEUS (80.22 ± 76.49 cm3). CONCLUSIONS: Some pre-ablation characteristics of uterine fibroids on CEUS were associated with NPV after MRgFUS. CEUS may contribute to the evaluation of ablative outcomes and patient selection, similar to MR imaging. KEY POINTS: • Contrast-enhanced ultrasonography (CEUS) is effective for selecting the appropriate uterine fibroids before MR-guided focused ultrasound (MRgFUS) ablation and evaluating non-perfusion volumes (NPV) after ablation, as a potential alternative to MR imaging. • Before ablation, entangled branch vessels, fast-in, and fast-out patterns on CEUS were significantly associated with NPV after MRgFUS. • No significant differences in NPV were detected between contrast-enhanced T1WI and CEUS after ablation.

The Clinical and MRI Effect of TNF-α Inhibitors in Spondyloarthritis Patients With Hip Involvement: A Real-World Observational Clinical Study.

Objectives: Hip involvement is an important cause of disability and poor prognosis in patients with spondyloarthritis (SpA). Tumor necrosis factor (TNF)-α inhibitor treatment has been demonstrated to be effective in SpA patients with hip arthritis; however, quantitative assessment using MRI in long-term follow-up needs further application and observation. Methods: A total of 239 patients were involved in this study. Methotrexate and sulfasalazine were given as basic treatment. In total, 165 patients received TNF-α inhibitors plus basic treatment, and 74 received basic treatment only, as controls. Clinical symptoms were assessed at baseline and at weeks 12, 24, and 52. MRI performances of hip arthritis, including bone marrow edema (BME) and synovitis, were quantitatively assessed using the Hip Inflammation MRI Scoring System (HIMRISS). Results: The clinical values of erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Harris hip score, and Ankylosing Spondylitis Disease Activity Score (ASDAS)-ESR in both groups showed significant clinical remission at week 52 (p < 0.001). However, the change in disease activity levels at week 52 in the control group was significantly worse than in the TNF-α inhibitor group. At week 52, MRI showed a significant remission trend in the TNF-α inhibitor group versus baseline, and total HIMRISS scores were significantly decreased (26.49 ± 10.37 vs. 20.59 ± 9.41, p < 0.001); the control group only had slight improvement (p < 0.05). Conclusions: TNF-α inhibitors could significantly improve clinical and MRI manifestations of hip involvement in patients with SpA. Quantitative MRI assessment combined with clinical assessment can be used to accurately evaluate the treatment effect of TNF-α in SpA patients with hip involvement to help guide targeted treatment.