Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma.

Objective: Invasive lung adenocarcinoma（ILA） with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution（SR） enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning. Methods: Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC). Result: A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility. Conclusion: The study successfully constructed and evaluated a deep learning（DL）-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.

Evaluating the association between vascular remodeling and plaque calcification patterns of the carotid artery and its effects on ischemic symptoms using CT angiography.

Background: Arterial remodeling is a compensatory mechanism of the vessel wall in response to atherosclerotic plaque growth. However, the clinical significance of vascular remodeling of carotid lesions remains unclear. Through this study, we aimed to evaluate the association between vascular remodeling and ischemic symptoms in patients with an internal carotid artery (ICA) stenosis degree ≥50%, considering the differences in plaque calcification patterns. Methods: This retrospective cross-sectional study included adult patients with moderate-to-severe proximal ICA stenosis associated with atherosclerotic plaques admitted to the Zhejiang Hospital between September 2018 and March 2023. Parameters such as lumen diameter, plaque calcification types, calcium scores, and calcification thickness were assessed using non-contrast and contrast-enhanced computed tomography angiography (CTA). The remodeling ratio (RR) was calculated by dividing the maximum distance of the proximal ICA between the inner border of the arterial lumen at the plaque site and the outer borders of the plaque by the luminal diameter. Atherosclerosis risk factors and medications were recorded. The Mann-Whitney U test or chi-square test was used to compare the differences between groups. Correlations were measured using Pearson's correlation coefficient. Predictors of ischemic symptoms were assessed using multivariable logistic regression analysis, with results expressed as odds ratio (ORs) with 95% confidence intervals (CIs). A P value less than 0.05 (two-sided) was considered to indicate statistical significance The differences in RR among plaque calcification types and the association between vascular remodeling and clinical symptoms were analyzed. Results: A total of 242 ICAs in 196 patients were included in this study, and 84 were symptomatic and 158 were asymptomatic. The RR in symptomatic ICA [median, 1.31 (interquartile range, 1.17-1.68)] was significantly greater than that in asymptomatic group [median, 1.20 (interquartile range, 1.05-1.45)], P=0.006). Significant differences in RR existed among plaque calcification types, among which type 5 and 6 plaques had the highest RR. About 71.5% (173/242) of all ICAs showed positive remodeling. Significant correlations were observed between RR and ischemic symptoms and between positive remodeling and calcification thickness (P<0.05 for all variables). On multivariable logistic regression analysis, calcification thickness remained significantly associated with positive remodeling of carotid arteries (OR 2.30; 95% CI: 1.06-5.01; P=0.036). Conclusions: Arterial remodeling exists in the ICA. A significant association between arterial positive remodeling and plaque calcification thickness was established. RR helps predict ischemic symptoms. The results of our study suggest that arterial remodeling serves as a novel measure to help ascertain the risk stratification of ischemic events in carotid atherosclerotic disease.

Cholinergic hyperintensity pathways are associated with cognitive performance in patients with asymptomatic carotid artery stenosis.

OBJECTIVES: We aimed to determine whether asymptomatic carotid artery stenosis (ACS) induced cognitive impairments were related to the cholinergic hyperintensity pathway. METHODS: This cross-sectional study included patients with moderate-to-severe ACS, who were categorized into mild cognitive impairment (MCI) and normal cognition groups on the basis of Montreal Cognitive Assessment (MoCA) scores. The cholinergic pathway hyperintensity scale (CHIPS), Fazekas, and medial temporal atrophy (MTA) scores were assessed. SPSS software was used for statistical analyses. RESULTS: A total of 117 ACS patients (70.89 ± 8.81 years) and 105 controls (67.87 ± 9.49 years) were evaluated (t = 2.46, p = 0.015). The ACS group showed a worse median Mini-Mental Status Examination (MMSE) score (z = -2.41, p = 0.016) and MoCA score (z = -3.51, p < 0.001), and a significantly higher median total CHIPS score (z = 4.88, p < 0.001) and mean Fazekas score (t = 2.39, p = 0.018). In the correlation analysis, the MoCA score showed a significant negative correlation with the CHIPS score (ρ = -0.41, p < 0.001) and Fazekas score (ρ = -0.31, p < 0.001) in ACS group. Logistic regression analyses suggested that CHIPS scores were risk factors for MCI in patients with ACS (odds ratio [OR] = 1.07, 95% Confidence Interval [CI]1.01-1.13 and controls (OR = 1.09, 95%CI 1.01-1.17), while the MTA and Fazekas scores showed no predictive power. The receiver operating characteristic curve showed that the area under the curve of the CHIPS score for predicting MCI was 0.71 in ACS group, but was only 0.57 in controls. CONCLUSIONS: Patients with ACS showed poorer cognitive performance and higher CHIPS and Fazekas scores. CHIPS, but not Fazekas, scores were risk factors for cognitive impairment and were a valuable factor to predict MCI in patients with ACS.

An adaptive weighted attention-enhanced deep convolutional neural network for classification of MRI images of Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is the second prevalent neurological diseases with a significant growth rate in incidence. Convolutional neural networks using structural magnetic resonance images (sMRI) are widely used for PD classification. However, the areas of change in the patient's MRI images are small and unfixed. Thus, capturing the features of the areas accurately where the lesions changed became a problem. METHOD: We propose a deep learning framework that combines multi-scale attention guidance and multi-branch feature processing modules to diagnose PD by learning sMRI T2 slice features. In this scheme, firstly, to achieve effective feature transfer and gradient descent, a deep convolutional neural network framework based on dense block is designed. Next, an Adaptive Weighted Attention algorithm is proposed, whose pursers is to extract multi branch and even diverse features. Finally, Dropout layer and SoftMax layer are added to the network structure to obtain good classification results and rich and diverse feature information. The Dropout layer is used to reduce the number of intermediate features to increase the orthogonality between features of each layer. The activation function SoftMax increases the flexibility of the neural network by increasing the degree of fitting to the training set and converting linear to nonlinear. RESULTS: The best performance of the proposed method an accuracy of 92%, a sensitivity of 94%, specificity of 90% and a F1 score of 95% respectively for identifying PD and HC. CONCLUSION: Experiments show that the proposed method can successfully distinguish PD and NC. Good classification results were obtained in PD diagnosis classification task and compared with advanced research methods.

Crossed pulmonary arteries associated with single atrium in an adult: a case report.

BACKGROUND: Crossed pulmonary arteries or single atrium is a rare form of cardiovascular anomaly. In previous studies, the anomalies are detected in infant or early adolescence, and infrequently seen in adult population. CASE PRESENTATION: We presented a case of the coexistence of two congenital anomalies in a 44-year-old woman who remained well tolerated and undiscovered until adulthood. Physical examination showed a grade III systolic murmur at the cardiac apex, and a grade II/III systolic murmur at left 2-3 intercostal space. An echocardiography revealed absence of atrial septal tissue. Dual-source CT angiography was performed for further evaluation of the great vessel. Except an enlarged single atrium, the imaging showed that the origination of the left pulmonary artery from the pulmonary trunk was superior to that of the right pulmonary artery. The branch pulmonary arteries then crisscrossed as they coursed to their respective lungs. The findings were illustrated by the right heart catheterization and then confirmed at surgery. CONCLUSIONS: To our knowledge, this is the first case report of crossed pulmonary arteries with single atrium as the only additional cardiac anomaly in an adult. Knowledge of this rare combination will be helpful in the differential diagnosis of congenital heart disease and assist the surgeon in treatment planning.