18F-FDG PET-CT for the prediction of mortality in patients with dermatomyositis and without malignant tumors: a pilot study.

Background: 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT) is typically used to screen malignancy in patients with dermatomyositis (DM). The aim of this study was to investigate the value of using PET-CT in assessing the prognosis of patients with DM and without malignant tumors. Methods: A total of 62 patients with DM who underwent 18F-FDG PET-CT were enrolled in the retrospective cohort study. Clinical data and laboratory indicators were obtained. The muscle max standardized uptake value (SUVmax), splenic SUVmax, target-to-background ratio (TBR) of the aorta, pulmonary highest value (hv)/SUVmax, epicardial fat volume (EFV), and coronary artery calcium (CAC) were measured using 18F-FDG PET-CT. The follow-up was conducted until March 2021, and the endpoint was death from any cause. Univariable and multivariable Cox regression analyses were used to analyze prognostic factors. The survival curves were produced with the Kaplan-Meier method. Results: The median duration of follow-up was 36 [interquartile range (IQR), 14-53] months. The survival rates were 85.2% and 73.4% for 1 and 5 years, respectively. A total of 13 (21.0%) patients died during a median follow-up of 7 (IQR, 4-15.5) months. Compared with the survival group, the death group had significantly higher levels of C-reactive protein [CRP; median (IQR), 4.2 (3.0, 6.0) vs. 6.30 (3.7, 22.8)], hypertension [7 (14.3%) vs. 6 (46.2%)], interstitial lung disease [ILD; 26 (53.1%) vs. 12 (92.3%)], positive anti-Ro52 antibody [19 (38.8%) vs. 10 (76.9%)], pulmonary FDG uptake [median (IQR), 1.8 (1.5, 2.9) vs. 3.5 (2.0, 5.8)], CAC [1 (2.0%) vs. 4 (30.8%)], and EFV [median (IQR), 74.1 (44.8, 92.1) vs. 106.5 (75.0, 128.5)] (all P values <0.001). Univariable and multivariable Cox analyses identified high pulmonary FDG uptake [hazard ratio (HR), 7.59; 95% confidence interval (CI), 2.08-27.76; P=0.002] and high EFV (HR, 5.86; 95% CI, 1.77-19.42; P=0.004) as independent risk factors for mortality. The survival rate was significantly lower in patients with the concurrent presence of high pulmonary FDG uptake and high EFV. Conclusions: Pulmonary FDG uptake and EFV detected with PET-CT were independent risk factors for death in patients with DM and without malignant tumors. Patients with the concurrent presence of high pulmonary FDG uptake and high EFV had a worse prognosis compared with patients with 1 or neither of these two risk factors. Early treatment should be applied in patients with concurrent presence of high pulmonary FDG uptake and high EFV to improve the survival rate.

Initiative global NILS control in source and mask optimization for process window enhancement.

Semiconductor processing is becoming more challenging as integrated circuit dimensions shrink. An increasing number of technologies are being developed for the purpose of ensuring pattern fidelity, and source and mask optimization (SMO) method has outstanding performances. In recent times, owing to the development of the process, more attention has been paid to the process window (PW). As a crucial parameter in lithography, the normalized image log slope (NILS) is strongly correlated with the PW. However, previous methods ignored the NILS in the inverse lithography model of the SMO. They regarded the NILS as the measurement index for forward lithography. This implies that the optimization of the NILS is the result of passive rather than active control, and the final optimization effect is unpredictable. In this study, the NILS is introduced in inverse lithography. The initial NILS is controlled by adding a penalty function to ensure that it continuously increases, thus increasing the exposure latitude and enhancing the PW. For the simulation, two masks typical of a 45-nm-node are selected. The results indicate that this method can effectively enhance the PW. With guaranteed pattern fidelity, the NILS of the two mask layouts increase by 16% and 9%, and the exposure latitudes increase by 21.5% and 21.7%.

Source and mask optimizing with a defocus antagonism for process window enhancement.

With the continuous reduction of critical dimension (CD) of integrated circuits, inverse lithography technology (ILT) is widely adopted for the resolution enhancement to ensure the fidelity of photolithography, and for the process window (PW) improvement to enlarge the depth of focus (DOF) and exposure latitude (EL). In the photolithography, DOF is a critical specification which plays a vital role for the robustness of a lithographical process. DOF has been investigated to evaluate the optimization quality of ILT, but there is not a clear scenario to optimize the DOF directly. In this paper, the source and mask optimization (SMO) based on defocus generative and adversarial method (DGASMO) is proposed, which takes the source, mask and defocus as variables, and the inverse imaging framework employs the Adam algorithm to accelerate the optimization. In the optimization process, the penalty term constantly pushes the defocus outward, while the pattern fidelity pushes the defocus term inward, and the optimal source and mask are constantly searched in the confrontation process to realize the control of DOF. Compared to SMO with the Adam method (SMO-Adam), the PW and DOF (EL = 15%) in DGASMO maximally increased 29.12% and 44.09% at 85 nm technology node, and the PW and DOF (EL = 2%) at 55 nm technology node maximally increased 190.2% and 118.42%. Simulation results confirm the superiority of the proposed DGASMO approach in DOF improvement, process robustness, and process window.

Right Atrial Fluorodeoxyglucose Uptake Is a Risk Factor for Stroke and Improves Prediction of Stroke Above the CHA2DS2-VASc Score in Patients With Atrial Fibrillation.

Background: Atrial fibrillation (AF) is a common arrhythmia, and its most severe and dreaded complication is stroke. The CHA2DS2-VASc score is currently recommended for stroke risk assessment in AF. We aimed to explore the relationship between atrial FDG uptake and stroke and whether atrial FDG uptake could provide incremental value above the CHA2DS2-VAS score to predict stroke in AF by 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT). Materials and Methods: From September 2017 to December 2020, we retrospectively enrolled 230 patients (115 with AF and 115 without AF as the non-AF group, matched for the date of PET/CT examination and the basic characteristics of the patient) who underwent 18F-FDG PET/CT due to tumor screening or preoperative staging after prolonged fasting and followed up for at least 12 months from the date of PET/CT examination; the endpoint event is the occurrence of stroke. We visually and quantitatively analyzed 18F-FDG uptake in the right and left atria (RA/LA), right and left atrial appendage (RAA/LAA), right and left ventricle (RV/LV), and collected clinical features. In addition, according to the endpoint event (stroke), the enrolled population was divided into the stroke group and non-stroke group, and relevant clinical features and atrial FDG uptake indicators of the two groups were analyzed. Univariate and multivariate Cox regression analyzes were used to analyze the risk factors of stroke events. The Kaplan-Meier survival curve of atrial FDG uptake was drawn, and the log-rank method was used to compare the differences in the survival curves of the two groups. Receiver operating characteristic (ROC) curves were used to examine the discriminatory power of atrial FDG uptake in predicting stroke and determine whether the addition of atrial FDG uptake improves predictive value beyond the CHA2DS2-VASc score for stroke. Results: In the AF group, more than half of patients had RA FDG uptake and one-fifth had LA FDG uptake, while one patient had RA FDG uptake and two patients had LA FDG uptake in the non-AF group. In quantitative analysis, the maximum standardized uptake value (SUV max ) of the RA and LA in the AF group was significantly higher than that of the non-AF group (all P < 0.001). We followed up the patients for 28 ± 10 months, and finally, 31 patients had stroke. In the stroke group, atrial fibrillation, RA SUV max , RAA SUV max , LAA SUV max , age ≥ 75 years, and left atrial dilation were significantly higher than those of the non-stroke group (all P < 0.05). Multivariate Cox regression analysis showed that high RA SUV max (RA SUV max ≥ 2.62) was an independent risk factor for stroke (HR = 4.264, 95% CI 1.368-13.293, P = 0.012). By using the log-rank test, patients with high RA SUV max had a significantly higher incidence of stroke compared with patients with low RA SUV max (P < 0.001). Addition of high RA SUV max to the CHA2DS2-VASc score could predict stroke more effectively, with a larger AUC 0.790 (P < 0.001). Conclusion: This study found a significant correlation between atrial FDG uptake and AF, especially in RA. Meanwhile, RA FDG uptake is an independent risk factor for stroke, and patients with high RA SUV max have a significantly higher risk of stroke. Moreover, RA FDG uptake improves prediction of stroke above the CHA2DS2-VASc score in patients with AF.

A model combining rest-only ECG-gated SPECT myocardial perfusion imaging and cardiovascular risk factors can effectively predict obstructive coronary artery disease.

OBJECTIVE: The rest-only single photon emission computerized tomography (SPECT) myocardial perfusion imaging (MPI) had low sensitivity in diagnosing obstructive coronary artery disease (CAD). Improving the efficacy of resting MPI in diagnosing CAD has important clinical significance for patients with contraindications to stress. The purpose of this study was to develop and validate a model predicting obstructive CAD in suspected CAD patients, based on rest-only MPI and cardiovascular risk factors. METHODS: A consecutive retrospective cohort of 260 suspected CAD patients who underwent rest-only gated SPECT MPI and coronary angiography was constructed. All enrolled patients had stress MPI contraindications. Clinical data such as age and gender were collected. Automated quantitative analysis software QPS and QGS were used to evaluate myocardial perfusion and function parameters. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the variables and build the prediction model. RESULTS: Among the enrolled 260 patients with suspected CAD, there were 95 (36.5%, 95/260) patients with obstructive CAD. The prediction model was presented in the form of a nomogram and developed based on selected predictors, including age, sex, SRS ≥ 4, SMS ≥ 2, STS ≥ 2, hypertension, diabetes, and hyperlipidemia. The AUC of the prediction model was 0.795 (95% CI: 0.741-0.843), which was better than the traditional models. The AUC calculated by enhanced bootstrapping validation (500 bootstrap resamples) was 0.785. Subsequently, the calibration curve (intercept = - 0.106; slope = 0.843) showed a good calibration of the model. The decision curve analysis (DCA) shows that the constructed clinical prediction model had good clinical applications. CONCLUSIONS: In patients with suspected CAD and contraindications to stress MPI, a prediction model based on rest-only ECG-gated SPECT MPI and cardiovascular risk factors have been developed and validated to predict obstructive CAD effectively.

MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification.

The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset.

Incremental value of epicardial fat volume to coronary artery calcium score and traditional risk factors for predicting myocardial ischemia in patients with suspected coronary artery disease.

BACKGROUND: Epicardial fat volume (EFV) has been reported to be associated with coronary artery disease (CAD). CAD is the leading cause of myocardial ischemia and myocardial ischemia is closely related to major adverse cardiovascular events. We hypothesized that EFV could provide incremental value to traditional risk factors and coronary artery calcium score (CACS) in predicting myocardial ischemia in Chinese patients with suspected CAD. METHODS: We retrospectively studied 204 Chinese patients with suspected CAD who underwent single-photon emission computerized tomography-myocardial perfusion imaging (SPECT-MPI) combined with computed tomography (CT). Pericardial contours were manually defined, and EFV was automatically calculated. A reversible perfusion defect with summed difference score (SDS) ≥ 2 was defined as myocardial ischemia. RESULTS: The myocardial ischemia group had higher EFV than normal MPI group (137.80 ± 34.95cm3 vs. 106.63 ± 29.10 cm3, P < .001). In multivariable logistic regression analysis, high EFV was significantly associated with myocardial ischemia [odds ratio (OR): 8.30, 95% CI: 3.72-18.49, P < .001]. Addition of EFV to CACS and traditional risk factors could predict myocardial ischemia more effectively, with larger AUC .82 (P < .001), positive net reclassification index .14 (P = .04) and integrated discrimination improvement .14 (P < .001). The bootstrap resampling method (times = 500) was used to internally validation and calculate the 95% confidence interval (CI) of the AUC (95% CI .75-.87). The calibration curve for the probability of myocardial ischemia demonstrated good agreement between prediction and observation. CONCLUSIONS: In Chinese patients with suspected CAD, EFV was significantly associated with myocardial ischemia, and improved prediction of myocardial ischemia above traditional risk factors and CACS.

Association of Epicardial Fat Volume With Increased Risk of Obstructive Coronary Artery Disease in Chinese Patients With Suspected Coronary Artery Disease.

Background Epicardial adipose tissue may be associated with the pathogenesis of coronary artery disease (CAD), but its effect on obstructive CAD risk is uncertain. Therefore, we aimed to examine the relationship between epicardial adipose tissue and obstructive CAD in Chinese patients with suspected CAD. Methods and Results The present study enrolled 194 consecutive inpatients with suspected CAD who underwent both noncontrast computed tomography and coronary angiography. We measured epicardial fat volume (EFV) and evaluated its association with obstructive CAD, which was defined as coronary stenosis severity ≥70%. Overall, 44.3% patients had obstructive CAD and tend to have higher EFV. Age, body mass index, triglycerides, incidence of hypertension, and hyperlipidemia were higher across tertiles of EFV (P for trend <0.05). In univariate regression analysis, a per-SD increase in EFV was independently associated with obstructive CAD (odds ratio [OR], 2.31; 95% CI, 1.61-3.32; P<0.001). Consistent with these findings, EFV was still significantly related to obstructive CAD as continuous variable after adjustment for all traditional risk factors and coronary artery calcium (OR per SD, 2.82; 95% CI, 1.68-4.74; P<0.001). Generalized additive model indicated that EFV was linearly associated with risk of obstructive CAD. E-value analysis suggested robustness to unmeasured confounding. Conclusions Our results suggested that in Chinese patients with suspected CAD, EFV was significantly and positively associated with the risk of obstructive CAD, independent of traditional risk factors and coronary artery calcium.

Incremental value of regional wall motion abnormalities for detecting obstructive coronary artery disease by rest-only electrocardiogram-gated single-photon emission computerized tomography myocardial perfusion imaging in suspected coronary artery disease patients.

OBJECTIVE: The purpose of this study was to determine whether regional wall motion (WM) abnormalities by rest-only 99mtechnetium-sestamibi (99mTc-MIBI) electrocardiogram (ECG)-gated single-photon emission computerized tomography (SPECT) myocardial perfusion imaging (MPI) had incremental diagnostic value for detecting obstructive coronary artery disease (CAD) in suspected CAD patients. METHODS: This study retrospectively studied 255 consecutive suspected CAD patients who underwent rest-only ECG-gated SPECT MPI and were performed coronary angiography within 3 months. Obstructive CAD was defined as ≥70% narrowing of the inner diameter of the left anterior descending coronary artery, left circumflex coronary artery and right coronary artery or their main branches and ≥50% narrowing of the left main coronary artery. QPS and QGS were used to assess rest perfusion and WM. Summed rest score ≥4 and summed motion score ≥2 exhibited in two consecutive segments in one territory was considered abnormal. RESULTS: The sensitivity of the combination of perfusion and regional WM abnormalities was significantly higher than perfusion alone for detecting obstructive CAD (46.8% vs. 30.9%; P < 0.001), with similar specificity (91.9% vs. 95.0; P = 0.063). The global chi-square value of combination of perfusion and WM increased from 31.40 to 50.71 (P < 0.001) compared to perfusion. The sensitivity of combination of perfusion and WM for detecting obstructive CAD in patients with multivessel disease was higher than single-vessel disease (56.1% vs. 25.0%; P < 0.001), with similar specificity. CONCLUSION: Regional WM abnormalities at rest, as shown by rest-only 99mTc-MIBI ECG-gated SPECT MPI, have additional diagnostic value over perfusion alone for detecting obstructive CAD in suspected CAD patients.