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Summary: This study investigated the abnormal dynamic functional connectivity (dFC) variability of the thalamo‐cortical circuit in patients with obstructive sleep apnea (OSA) and explored the relationship between these changes and the clinical characteristics of patients with OSA. A total of 91 newly diagnosed patients with moderate‐to‐severe OSA and 84 education‐matched healthy controls (HCs) were included. All participants underwent neuropsychological testing and a functional magnetic resonance imaging scan. We explored the thalamo‐cortical dFC changes by dividing the thalamus into 16 subregions and combining them using a sliding‐window approach. Correlation analysis assessed the relationship between dFC variability and clinical features, and the support vector machine method was used for classification. The OSA group exhibited increased dFC variability between the thalamic subregions and extensive cortical areas, compared with the HCs group. Decreased dFC variability was observed in some frontal‐occipital‐temporal cortical regions. These dFC changes positively correlated with daytime sleepiness, disease severity, and cognitive scores. Altered dFC variability contributed to the discrimination between patients with OSA and HCs, with a classification accuracy of 77.8%. Our findings show thalamo‐cortical overactivation and disconnection in patients with OSA, disrupting information flow within the brain networks. These results enhance understanding of the temporal variability of thalamo‐cortical circuits in patients with OSA. [ABSTRACT FROM AUTHOR]

Purpose: To investigate dynamic functional connectivity (dFC) within the cerebellar‐whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients. Methods: Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum‐whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed. Results: Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep. Conclusion: Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high‐order and low‐level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma, a leading cause of cancer-related mortality, demands precise prognostic indicators for effective management. The presence of spread through air space (STAS) indicates adverse tumor behavior. However, comparative differences between 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography(PET)/computed tomography(CT) and CT in predicting STAS in lung adenocarcinoma remain inadequately explored. This retrospective study analyzes preoperative CT and 18F-FDG PET/CT features to predict STAS, aiming to identify key predictive factors and enhance clinical decision-making. Methods: Between February 2022 and April 2023, 100 patients (108 lesions) who underwent surgery for clinical lung adenocarcinoma were enrolled. All these patients underwent 18F-FDG PET/CT, thin-section chest CT scan, and pathological biopsy. Univariate and multivariate logistic regression was used to analyze CT and 18F-FDG PET/CT image characteristics. Receiver operating characteristic curve analysis was performed to identify a cut-off value. Results: Sixty lesions were positive for STAS, and 48 lesions were negative for STAS. The STAS-positive was frequently observed in acinar predominant. However, STAS-negative was frequently observed in minimally invasive adenocarcinoma. Univariable analysis results revealed that CT features (including nodule type, maximum tumor diameter, maximum solid component diameter, consolidation tumor ratio, pleural indentation, lobulation, spiculation) and all 18F-FDG PET/CT characteristics were statistically significant difference in STAS-positive and STAS-negative lesions. And multivariate logistic regression results showed that the maximum tumor diameter and SUVmax were the independent influencing factors of CT and 18F-FDG PET/CT in STAS, respectively. The area under the curve of maximum tumor diameter and SUVmax was 0.68 vs. 0.82. The cut-off value for maximum tumor diameter and SUVmax was 2.35 vs. 5.05 with a sensitivity of 50.0% vs. 68.3% and specificity of 81.2% vs. 87.5%, which showed that SUVmax was superior to the maximum tumor diameter. Conclusion: The radiological features of SUVmax is the best model for predicting STAS in lung adenocarcinoma. These radiological features could predict STAS with excellent specificity but inferior sensitivity.Highlights: • Evaluated PET/CT and CT features for predicting STAS in lung adenocarcinoma. • SUVmax and maximum tumor diameter were independent influential factors for STAS(+) in lung adenocarcinoma. • SUVmax showed higher predictive efficacy than maximum tumor diameter. • Radiological characteristics may serve as noninvasive biomarkers for STAS prediction in lung adenocarcinoma. [ABSTRACT FROM AUTHOR]

Objective: The majority of previous neuroimaging studies have reported both structural and functional changes in COPD, whereas the intrinsic low-frequency oscillations changes and the relationship between the abnormal brain regions and the clinical performances remain unknown. The present study was conducted with the aim of evaluating the intrinsic brain activity in COPD patients using the amplitude of low-frequency fluctuation (ALFF) method. Methods: All participants, including 19 stable patients with COPD and 20 normal controls (NCs) matched in age, sex, and education, underwent resting-state functional MRI scans and performed cognitive function tests and respiratory functions tests. The local spontaneous brain activity was examined using the voxel-wise ALFF. Pearson’s correlation analysis was used to investigate the relationships between the brain regions with altered ALFF signal values and the clinical features in COPD patients. Results: Compared with the NCs, COPD patients showed significantly lower cognitive function scores. Also, lower ALFF areas in the cluster of the posterior cingulate cortex (PCC) and precuneus, as well as a higher ALFF area in the brainstem were also found in COPD patients. The mean ALFF values in the PCC, precuneus, and brainstem showed high sensitivity and specificity in operating characteristic curves analysis, which might have the ability to distinguish COPD from NCs. Meanwhile, the mean signal values of the lower ALFF cluster displayed significant positive correlations with FEV1/FVC proportion and significant negative correlation with PaCO2; the higher ALFF cluster showed significant positive correlation with FEV1 proportion in COPD. Conclusion: According to the results of the present study, the COPD patients showed abnormal intrinsic brain activities in the precuneus, PCC, and brainstem, which might provide useful information to better understand the underlying pathophysiology of cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: The estrogen receptor (ER) serves as a pivotal indicator for assessing endocrine therapy efficacy and breast cancer prognosis. Invasive biopsy is a conventional approach for appraising ER expression levels, but it bears disadvantages due to tumor heterogeneity. To address the issue, a deep learning model leveraging mammography images was developed in this study for accurate evaluation of ER status in patients with breast cancer., Objectives: To predict the ER status in breast cancer patients with a newly developed deep learning model leveraging mammography images., Materials and Methods: Datasets comprising preoperative mammography images, ER expression levels, and clinical data spanning from October 2016 to October 2021 were retrospectively collected from 358 patients diagnosed with invasive ductal carcinoma. Following collection, these datasets were divided into a training dataset (n = 257) and a testing dataset (n = 101). Subsequently, a deep learning prediction model, referred to as IP-SE-DResNet model, was developed utilizing two deep residual networks along with the Squeeze-and-Excitation attention mechanism. This model was tailored to forecast the ER status in breast cancer patients utilizing mammography images from both craniocaudal view and mediolateral oblique view. Performance measurements including prediction accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves (AUCs) were employed to assess the effectiveness of the model., Results: In the training dataset, the AUCs for the IP-SE-DResNet model utilizing mammography images from the craniocaudal view, mediolateral oblique view, and the combined images from both views, were 0.849 (95% CIs: 0.809-0.868), 0.858 (95% CIs: 0.813-0.872), and 0.895 (95% CIs: 0.866-0.913), respectively. Correspondingly, the AUCs for these three image categories in the testing dataset were 0.835 (95% CIs: 0.790-0.887), 0.746 (95% CIs: 0.793-0.889), and 0.886 (95% CIs: 0.809-0.934), respectively. A comprehensive comparison between performance measurements underscored a substantial enhancement achieved by the proposed IP-SE-DResNet model in contrast to a traditional radiomics model employing the naive Bayesian classifier. For the latter, the AUCs stood at only 0.614 (95% CIs: 0.594-0.638) in the training dataset and 0.613 (95% CIs: 0.587-0.654) in the testing dataset, both utilizing a combination of mammography images from the craniocaudal and mediolateral oblique views., Conclusions: The proposed IP-SE-DResNet model presents a potent and non-invasive approach for predicting ER status in breast cancer patients, potentially enhancing the efficiency and diagnostic precision of radiologists., Competing Interests: None., (AJTR Copyright © 2024.)

Objective: To discuss the soft palate, hard palate and mandibule for the pathogenesis and clinical treatment of patients who have obstructive sleep apnea hypopnea syndrome., Method: There were 52 patients with OSAHS diagnosed by the PSG and 32 cases of normal healthy volunteers in our hospital. All objects were given 128 slice spiral CT scan of the upper airway. We measured the related indexes of the soft palate, hard palate and mandible, then analysed statistical differences between them and did Pearson correlation analysis with apnea hypoventilation index (AHI), average blood oxygen saturation (MSaO2) in PSG., Result: Compared with the control group, the soft palate length [(37.93 ± 5.20)mm VS (33.52 ± 4.25)mm] and the distance between mandible with cervical vertebra [(75.00 ± 7.10)mm VS (69.93 ± 5.58)mm] increased significantly, but the distance of trailing edge of hard palate to inferior margin of slope significantly reduced [(42.57 ± 4.52)mm VS (45.80 ± 2.94)mm, P < 0.01] in patients with OSAHS. Each measurement associated with AHI and MSaO2 had no significant correlation (P > 0.05)., Conclusion: The soft palate, hard palate and mandibular are some of the important risk factors of OSAHS, and they also have reference value for the choice of clinical treatment operation. Preoperative CT examination can offer help in the treatment of OSAHS.