Predictive value of CT and 18F-FDG PET/CT features on spread through air space in lung adenocarcinoma.

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.

Abnormal temporal variability of thalamo-cortical circuit in patients with moderate-to-severe obstructive sleep apnea.

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.

Altered functional connectivity of cerebellar subregions in male patients with obstructive sleep apnea: A resting-state fMRI study.

PURPOSE: Previous studies have demonstrated impaired cerebellar function in patients with obstructive sleep apnea (OSA), which is associated with impaired cognition. However, the effects of OSA on resting-state functional connectivity (FC) in the cerebellum has not been determined. The purpose of this study was to investigate resting-state FC of the cerebellar subregions and its relevance to clinical symptoms in patients with OSA. METHODS: Sixty-eight patients with OSA and seventy-two healthy controls (HCs) were included in the study. Eight subregions of the cerebellum were selected as regions of interest, and the FC values were calculated for each subregion with other voxels. A correlation analysis was performed to examine the relationship between clinical and cognitive data. RESULTS: Patients with OSA showed higher FC in specific regions, including the right lobule VI with the right posterior middle temporal gyrus and right angular gyrus, the right Crus I with the bilateral precuneus/left superior parietal lobule, and the right Crus II with the precuneus/right posterior cingulate cortex. Furthermore, the oxygen depletion index was negatively correlated with aberrant FC between the right Crus II and the bilateral precuneus / right posterior cingulate cortex in OSA patients (p = 0.004). CONCLUSION: The cerebellum is functionally lateralized and closely linked to the posterior default mode network. Higher FC is related to cognition, emotion, language, and sleep in OSA. Abnormal FC may offer new neuroimaging evidence and insights for a deeper comprehension of OSA-related alterations.

Microstructural Changes in the Cerebral White Matter After 12 Months of CPAP Treatment for Moderate to Severe Obstructive Sleep Apnoea: A TBSS Study.

Introduction: Continuous positive airway pressure (CPAP) therapy improves clinical symptoms in patients with obstructive sleep apnea (OSA); however, the mechanism of this clinical improvement and how it may be associated with the restoration of white matter (WM) structures in the brain is unclear. Therefore, this study investigated the relationship between the structural recovery of brain WM and improvements in cognitive function and emotion after long-term (12 months) CPAP treatment in patients with OSA. Methods: We collected data from 17 patients with OSA before and 12 months after CPAP treatment, including sleep monitoring, clinical assessment, and diffusion tensor imaging (DTI) magnetic resonance imaging. Results: We observed a partial reversible recovery of brain WM (mean and radial diffusion coefficients) after treatment. This recovery involved the commissural fibers (cingulum, body of corpus callosum), projection fibers (retrolenticular part of the internal capsule, posterior thalamic radiation, posterior limb of the internal capsule, superior corona radiata, posterior corona radiata), association fibers (external capsule, superior longitudinal fasciculus, inferior longitudinal fasciculus), and other regions. In addition, the improvements in WM fibers in one part of the brain significantly were correlated with the Hamilton Anxiety Scale and Hamilton Depression Scale scores. Discussion: Our results suggest that reversible recovery of reduced brain WM integrity due to OSA may require longer CPAP treatment. Moreover, changes in the integrity of the commissural fibers were associated with emotion regulation. These restored WM areas may explain the cognitive and mood improvements observed after OSA treatment.

Deep learning modeling using mammography images for predicting estrogen receptor status in breast cancer.

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.

Functional Connectivity Changes in Amygdala Subregions of Obstructive Sleep Apnea Patients After Six Months of Continuous Positive Airway Pressure Treatment.

Purpose: Previous studies demonstrated that there was abnormal functional connectivity (FC) in the amygdala subregions in obstructive sleep apnea (OSA), which was associated with cognitive function. However, it is not clear whether these abnormalities can be improved after continuous positive airway pressure (CPAP) treatment. Therefore, the aim of this research was to investigate the changes in FC of amygdala subregions with other brain regions after 6 months of CPAP treatment (post-CPAP) in patients with OSA. Patients and Methods: Fifteen OSA patients underwent Magnetic Resonance Imaging prior to CPAP treatment (pre-CPAP) and following CPAP treatment. The amygdala was divided into six subregions, including bilateral dorsal amygdala (DA), medial amygdala (MA) and ventral amygdala (VA). The FC was calculated by using the amygdala subregions as seeds. A paired sample T-test was employed to assess alterations in the amygdala subregions FC of pre-CPAP and post-CPAP OSA patients, and correlation analysis was then conducted to evaluate the association between the changed FC and clinical assessment. Results: Compared to pre-CPAP OSA patients, post-CPAP OSA patients displayed an enhanced FC between the left DA and the right posterior cingulate cortex (PCC), whereas the FC between the left MA and the right postcentral gyrus, and between the right MA and the left middle frontal gyrus, decreased. Moreover, significant correlation between the FC value of left DA-right PCC and Hamilton Anxiety Inventory scores was found in pre-CPAP OSA patients. Conclusion: Altered FC between the amygdala subregions and other brain regions in OSA patients induced by CPAP treatment was related to cognitive, emotional, and sensorimotor function. Our study found altered FC between amygdala subregions and cognitive and motor-related brain regions in post-CPAP OSA patients, providing potential neuroimaging indicators for CPAP treatment.

Abnormal dynamic functional connectivity and topological properties of cerebellar network in male obstructive sleep apnea.

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.