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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]

Introduction: Extirpation of multiple head and neck paragangliomas carries challenge due to close anatomic relationships with critical neurovascular bundles. Objectives: This study aims to assess whether the application of 3-D models can assist with surgical planning and treatment of these paragangliomas, decrease surgically related morbidity and mortality. Methods: Fourteen patients undergoing surgical resection of multiple head and neck paragangliomas were enrolled in this study. A preoperative 3-D model was created based on radiologic data, and relevant critical anatomic relationships were preoperatively assessed and intraoperatively validated. Results: All 14 patients presented with multiple head and neck paragangliomas, including bilateral carotid body tumors (CBT, n = 9), concurrent CBT with glomus jugulare tumors (GJT, n = 4), and multiple vagal paragangliomas (n = 1). Ten patients underwent genomic analysis and all harbored succinate dehydrogenase complex subunit D (SDHD) mutations. Under guidance of the 3-D model, the internal carotid artery (ICA) was circumferentially encased by tumor on 5 of the operated sides, in 4 (80%) of which the tumor was successfully dissected out from the ICA, whereas ICA reconstruction was required on one side (20%). Following removal of CBT, anterior rerouting of the facial nerve was avoided in 3 (75%) of 4 patients during the extirpation of GJT with assistance of a 3-D model. Two patients developed permanent postoperative vocal cord paralysis. There was no vessel rupture or mortality in this study cohort. Conclusion: The 3-D model is beneficial for establishment of a preoperative strategy, as well as planning and guiding the intraoperative procedure for resection of multiple head and neck paragangliomas. [ABSTRACT FROM AUTHOR]

Background: Caudal pneumatization of the pterygoid process may impact endonasal exposure of the lateral recess of sphenoid sinus (LRSS). Objectives: This study aims to explore the implications of a pneumatized pterygoid process for an endonasal transpterygoid approach to the LRSS and to define strategies regarding the preservation or sacrifice of the vidian nerve. Methods: Dissection of the LRSS (11 sides) was performed on 6 cadaveric specimens, preselected for the radiographic presence of an LRSS. In addition, the dimensions of the LRSS were measured on the deidentified CT images of 120 patients (240 sides). The sphenoid sinus was subdivided into 3 categories: Type 1 (no identifiable LRSS), Type 2 (lateral pneumatization of the greater wing above the vidian canal), and Type 3 (pneumatization of both the greater wing and the pterygoid process). Results: On the cadaveric specimens, a Type 2 pneumatization often allowed access to the LRSS above the level of the vidian canal; thus, sparing the vidian neurovascular bundle. In Type 3 pneumatization, a frontal corridor through the pterygoid base could be created to reach the LRSS with preservation of the vidian nerve. Extreme Type 3 pneumatization, however, required the transposition or sacrifice of the vidian nerve to facilitate a full direct access to the superolateral LRSS. Measurements on CT images revealed that the extent of caudal pneumatization of the pterygoid process had no statistically significant correlation with the superolateral extension of the lateral recess in patients with Type 3 LRSS (P >.05). Conclusion: Pneumatization of the LRSS toward a caudal or superolateral direction may develop independent from each other. Caudal pneumatization of the pterygoid process seems to variably impact the endonasal exposure of the LRSS. [ABSTRACT FROM AUTHOR]

Objective: To build a clinical–radiomics model based on noncontrast computed tomography images to identify the risk of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) following intravenous thrombolysis (IVT). Materials and methods: A total of 517 consecutive patients with AIS were screened for inclusion. Datasets from six hospitals were randomly divided into a training cohort and an internal cohort with an 8:2 ratio. The dataset of the seventh hospital was used for an independent external verification. The best dimensionality reduction method to choose features and the best machine learning (ML) algorithm to develop a model were selected. Then, the clinical, radiomics and clinical–radiomics models were developed. Finally, the performance of the models was measured using the area under the receiver operating characteristic curve (AUC). Results: Of 517 from seven hospitals, 249 (48%) had HT. The best method for choosing features was recursive feature elimination, and the best ML algorithm to build models was extreme gradient boosting. In distinguishing patients with HT, the AUC of the clinical model was 0.898 (95% CI 0.873–0.921) in the internal validation cohort, and 0.911 (95% CI 0.891–0.928) in the external validation cohort; the AUC of radiomics model was 0.922 (95% CI 0.896–0.941) and 0.883 (95% CI 0.851–0.902), while the AUC of clinical–radiomics model was 0.950 (95% CI 0.925–0.967) and 0.942 (95% CI 0.927–0.958) respectively. Conclusion: The proposed clinical–radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke. Key points: Noncontrast computed tomography (NCCT) is valuable for predicting hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) treatment. Machine learning is vital for predicting HT. NCCT radiomics integrated with clinical factors could facilitate predicting HT. [ABSTRACT FROM AUTHOR]

Nasal septal deviations (NSD) have been categorized into 7 types. The effect of these different deviations on airflow pattern and warming function has not been fully investigated. The purpose of this study was to utilize a computational fluid dynamics approach to assess the impact of NSD of varying types on nasal airflow and warming function. Patients with each type of NSD were enrolled in the study, and a normal participant as the control. Using a computational fluid dynamics approach, modeling of nasal function was performed. Indices of nasal function including airflow redistribution, total nasal resistance, airflow velocity, and airflow temperature were determined. Among all types of NSD, the maximal velocity and total nasal resistance were markedly higher in type 4 and 7 deviations. The flow partition and velocity distribution were also altered in type 4 and 7 as well as type 2 and 6 deviations. Airflow in all categories of NSD was fully warmed to a similar degree. From a computational aerodynamics perspective, the type of septal deviation may contribute to altered airflow characteristics. However, warming function was similar between septal deviation types. Future studies will help to ascertain the functional importance of septal deviation types and the applicability of these computational studies. [ABSTRACT FROM AUTHOR]

Investigation of desorption of diffusion migration of native adsorbed gas in coal under the action of temperature has always been one of the important directions of coal mine safety research. Five coal samples from the Sichuan Furong mining area are studied under the different isothermal conditions (35°C, 30°C, 24.5°C, 36.2°C, and 40°C) by means of laboratory tests; the desorption, diffusion, and migration processes of adsorbed gas were studied; the escaped gas volume in the process of diffusion, the escaped gas pressure, and the change of the concentration of the gas component content are summarized; and the emitted gas volume curve will be eventually found to be constant under the isothermal process. In addition, the escaped gas concentration showed a trend of increasing first and then decreasing. Based on the CT scan slice of coal samples, a network model of coal fracture based on the Tyson polygon contraction method is established in this paper, and LS-DYNA software is used to analyze the transient diffusion behavior of methane under three isothermal conditions (24.5°C, 30°C, and 42°C), and the experimental results are verified. [ABSTRACT FROM AUTHOR]

Background: The functional significance of the posterior segment of the septum is not fully understood. Aims/Objectives: The purpose of this study was to evaluate the impact of posterior septum resection with varying extents on nasal airflow pattern and warming function, and to determine the appropriate resection extent on posterior septum without disturbing airflow pattern. Material and Methods: Three dimensional models were established from the nasal CT scan of a healthy female volunteer without nasal disease. The models included the normal, as well as 1 cm, 2 cm, and 3 cm extent of posterior septum resection by virtual surgery. Using computational fluid dynamics, the airflow characteristics such as average and peak airflow velocity, average wall shear stress, average temperature and turbulence kinetic energy were recorded and compared among the four models. Results: By comparison with the normal model, the virtual surgeries with varying extents of posterior septum resection (1 cm, 2 cm, and 3 cm), demonstrated little alteration on indices of the average and peak airflow velocity, average wall shear stress, airflow temperature and turbulence kinetic energy. However, a statistical difference of the abovementioned indices existed when compared between varying planes of the nasal cavity (p <.001), although this did not appear to be significantly influenced by the degree of posterior septectomy. Conclusions and significance: From an aerodynamics perspective, little disturbance on airflow pattern and warming function of nasal cavity could be detected with increasing resection of posterior septum (up to 3 cm). These results may imply that resection of the posterior septum (up to 3 cm) has little impact on nasal function. [ABSTRACT FROM AUTHOR]

Brain stroke is a leading cause of disability and death worldwide, and early diagnosis and treatment are critical to improving patient outcomes. Current stroke diagnosis methods are subjective and prone to errors, as radiologists rely on manual selection of the most important CT slice. This highlights the need for more accurate and reliable automated brain stroke diagnosis and localization methods to improve patient outcomes. In this study, we aimed to enhance the vision transformer architecture for the multi-slice classification of CT scans of each patient into three categories, including Normal, Infarction, Hemorrhage, and patient-wise stroke localization, based on end-to-end vision transformer architecture. This framework can provide an automated, objective, and consistent approach to stroke diagnosis and localization, enabling personalized treatment plans based on the location and extent of the stroke. We modified the Vision Transformer (ViT) in combination with neural network layers for the multi-slice classification of brain CT scans of each patient into normal, infarction, and hemorrhage classes. For stroke localization, we used the ViT architecture and convolutional neural network layers to detect stroke and localize it by bounding boxes for infarction and hemorrhage regions in a patient-wise manner based on multi slices. Our proposed framework achieved an overall accuracy of 87.51% in classifying brain CT scan slices and showed high precision in localizing the stroke patient-wise. Our results demonstrate the potential of our method for accurate and reliable stroke diagnosis and localization. Our study enhanced ViT architecture for automated stroke diagnosis and localization using brain CT scans, which could have significant implications for stroke management and treatment. The use of deep learning algorithms can provide a more objective and consistent approach to stroke diagnosis and potentially enable personalized treatment plans based on the location and extent of the stroke. Further studies are needed to validate our method on larger and more diverse datasets and to explore its clinical utility in real-world settings. • Innovating Vision Transformer for Accurate Multi-Slice Classification • ViT-CNN Integration for Precise Stroke Detection & Localization. • Innovative Deep Learning for Multi-Slice Brain CT Scan Analysis without Radiologist's Prominent Slice Selection. • High Accuracy in Stroke Classification & Precise Localization • Personalized Treatment Plans by considering the specific location and extent of the brain stroke [ABSTRACT FROM AUTHOR]