Superior scapular location: An overlooked albeit frequent finding in elastofibroma dorsi.

PURPOSE: To explore the frequency of superior scapular elastofibroma dorsi in a large patient series with elastofibroma dorsi. METHODS: 136 chest CTs from January 2016 to July 2022 reporting elastofibroma dorsi were retrospectively analyzed. Three radiologists assessed the number, size, and location of elastofibroma dorsi. Continuous variables underwent two-tailed t-tests with p < 0.05. Inter-observer agreement was assessed by using Cohen's Kappa values. RESULTS: In 136 patients (mean age, 75.9 +/- 9.8 years; 117 female), 330 elastofibroma dorsi were found. Six (4.4 %) patients had single, 87 (64 %) double, 22 (16.2 %) triple and 21 (15.4 %) quadruple lesions. All single and double lesions were in the inferior scapular regions. 43 (31.6 %) patients had superior scapular lesions in addition to inferior scapular elastofibroma dorsi. Inferior scapular elastofibroma dorsi was significantly larger than superior scapular elastofibroma dorsi. The probability of a right superior lesion was significantly higher in patients with a larger right inferior lesion. Inter-observer agreement was very good for experienced radiologist (κ = 94.1) and good for other radiologists (κ = 79.4 and κ = 78). CONCLUSION: In contrast to current belief, superior scapular elastofibroma dorsi accompanying the typical inferior scapular lesions is not uncommon and can even manifest bilaterally. To the best of our knowledge, this is the first case series reporting prevalence of quadruple elastofibroma dorsi.

Different anatomical variations in the anterior segment of the right upper lung.

During a routine physical examination three years ago, a 47-year-old woman received a diagnosis of a nodule in her right upper lung. Since then, she has been regularly attending outpatient clinic appointments for follow-up. Over time, the nodule has shown gradual growth, leading to a suspicion of lung cancer. Through the use of enhanced CT imaging, a three-dimensional reconstruction was performed to examine the bronchi and blood vessels in the patient's chest. This reconstruction revealed several variations in the anatomy of the anterior segment of the right upper lobe. Specifically, the anterior segmental bronchus (B3) was found to have originated from the right middle lung bronchus. Additionally, the medial subsegmental artery of the anterior segmental artery (A3b) and the medial segmental artery (A5) were observed to share a common trunk. As for the lateral subsegmental artery of the anterior segmental artery (A3a), it was found to have originated from the right inferior pulmonary trunk. Furthermore, the apical subsegmental artery of the apical segmental artery (A1a) and the posterior segmental artery (A2) were found to have a shared trunk.

Posterior mediastinal leiomyosarcoma: Case report and literature review.

BACKGROUND: Posterior mediastinal leiomyosarcoma is an extremely rare malignant mesenchymal tumor with no special clinical symptoms, which is easily confused with some common tumors in the posterior mediastinum, affecting the accuracy of the first diagnosis by clinicians and delaying the treatment of patients. CASE SUMMARY: We report a 59-year-old woman with a space-occupying lesion in the posterior mediastinum. The patient was mistakenly diagnosed with lumbar muscle or vertebral body lesions due to chest and back pain and underwent conservative treatment, but her symptoms did not improve significantly and she gradually developed pain in both lower limbs. Chest computed tomography (CT) scan indicated the left lower lung paraspinal space and underwent standard single-aperture video-assisted thoracoscopic surgery (VATS), which was pathologically confirmed as posterior mediastinal leiomyosarcoma. CONCLUSION: Complete surgical resection of posterior mediastinal leiomyosarcoma can achieve good clinical results.

ADVANCES IN MINIMALLY INVASIVE SURGERY FOR PECTUS EXCAVATUM: ENHANCING OUTCOMES AND PATIENT CARE.

Pectus excavatum, also called sunken chest, is the most common deformation of the sternum (90%). The deformation is caused by the depression of the sternum and costal cartilages, which causes reduction of the chest cavity and dysfunction of cardio-pulmonary systems in it. Sunken chest is more common in males than females, prevalence is 5/1. Most of the cases appear in the first year of life, however severity of the pathology is formed during puberty. Etiopathogenesis, genetic factors, and associated diseases of Pectus Excavatum are various and are still the subject of study. The manifestation of the disease is determined by the degree of chest deformation, which is calculated using the "Haller index". Providing that a high degree of deformation can lead to pathological functioning of the cardiovascular and respiratory systems. The treatment of this condition is an urgent, complex, and developing issue. The main method of treatment for sunken chest is surgical intervention; However, in cases of mild degrees of the mentioned deformation, different approaches are used. Our goal is to discuss contrasting treatment techniques and present our improved repairing technique for sunken chest, which is performed in Georgia.

Mediastinal Rosai-Dorfman Disease with KRAS mutation case report and literature review.

BACKGROUND: Rosai-Dorfman Disease (RDD) is a rare self-limiting histiocytosis, more prevalent in children and young adults. It typically manifests as painless bilateral massive cervical lymphadenopathy but may also extend to extra-nodal sites, with intrathoracic RDD noted in 2% of cases. Distinguishing mediastinal RDD from thymoma on imaging poses challenges, underscoring the reliance on pathological features and immunohistochemical staining for diagnosis. CASE PRESENTATION: Patient, male, 33 years old, underwent lung a CT revealing an enlarged round soft tissue shadow in the anterior superior mediastinum, compared to a year ago. Surgical resection removed the entire mass, thymus, and part of the pericardium, confirming RDD on pathology. Genetic testing using second-generation testing technology identified a KRAS gene point mutation. CONCLUSIONS: No established treatment protocol currently exists for this disease. However, as genetic mutation research progresses, a novel therapeutic avenue is emerging: targeted therapy integrated with surgical interventions.

Morphological comparative study of canine and feline diaphragms: Effect of bodyweight, body condition score, gender, age and breed.

The canine diaphragm is a muscular and tendinous dome-like plate and is largely involved in digestive and respiratory functions. Very few studies compared morphology of the diaphragm between dogs and cats and no studies have investigated the effects of individual factors on this morphology. So the aim of this study was to (1) study the effects of individual factors on the morphology of the diaphragm and (2) to compare its morphology between cats and dogs. Surface measurements of 86 diaphragms were performed. The tendinous centre (TC), the lumbar part of the peripheral muscular (LP) and the sternal and costal parts of the peripheral muscular (SCPM) were measured. Measurement ratios (surface of anatomical part of the diaphragm/total surface of the diaphragm) were used for statistical analysis (TC%S, LP%S, SCPM%S). The SCPM%S are significantly lower, and the LP%S are significantly higher when bodyweight increases in dogs and cats. The TC%S are significantly lower when the body condition score of dogs increases. The SCPM%S are significantly higher, and therefore, the TC%S and LP%S lower in brachycephalic animals as opposed to mesocephalic animals. The TC%S are significantly higher in dogs than in cats and the SCPM%S are significantly higher in cats than in dogs. This study proposed a cartography of the diaphragm morphology in cats and dogs taking into account individual animal factors. Significant differences in the diaphragm morphology between cats and dogs and between mesocephalic and brachycephalic animals were found. Further studies are necessary to confirm these results and to investigate the consequences of these variations.

Dyssynchronous diaphragm contractions impair diaphragm function in mechanically ventilated patients.

BACKGROUND: Pre-clinical studies suggest that dyssynchronous diaphragm contractions during mechanical ventilation may cause acute diaphragm dysfunction. We aimed to describe the variability in diaphragm contractile loading conditions during mechanical ventilation and to establish whether dyssynchronous diaphragm contractions are associated with the development of impaired diaphragm dysfunction. METHODS: In patients receiving invasive mechanical ventilation for pneumonia, septic shock, acute respiratory distress syndrome, or acute brain injury, airway flow and pressure and diaphragm electrical activity (Edi) were recorded hourly around the clock for up to 7 days. Dyssynchronous post-inspiratory diaphragm loading was defined based on the duration of neural inspiration after expiratory cycling of the ventilator. Diaphragm function was assessed on a daily basis by neuromuscular coupling (NMC, the ratio of transdiaphragmatic pressure to diaphragm electrical activity). RESULTS: A total of 4508 hourly recordings were collected in 45 patients. Edi was low or absent (≤ 5 µV) in 51% of study hours (median 71 h per patient, interquartile range 39-101 h). Dyssynchronous post-inspiratory loading was present in 13% of study hours (median 7 h per patient, interquartile range 2-22 h). The probability of dyssynchronous post-inspiratory loading was increased with reverse triggering (odds ratio 15, 95% CI 8-35) and premature cycling (odds ratio 8, 95% CI 6-10). The duration and magnitude of dyssynchronous post-inspiratory loading were associated with a progressive decline in diaphragm NMC (p < 0.01 for interaction with time). CONCLUSIONS: Dyssynchronous diaphragm contractions may impair diaphragm function during mechanical ventilation. TRIAL REGISTRATION: MYOTRAUMA, ClinicalTrials.gov NCT03108118. Registered 04 April 2017 (retrospectively registered).

Network-based analysis identifies potential therapeutic ingredients of Chinese medicines and their mechanisms toward lung cancer.

Lung cancer is one of the most common malignant tumors around the world, which has the highest mortality rate among all cancers. Traditional Chinese medicine (TCM) has attracted increased attention in the field of lung cancer treatment. However, the abundance of ingredients in Chinese medicines presents a challenge in identifying promising ingredient candidates and exploring their mechanisms for lung cancer treatment. In this work, two network-based algorithms were combined to calculate the network relationships between ingredient targets and lung cancer targets in the human interactome. Based on the enrichment analysis of the constructed disease module, key targets of lung cancer were identified. In addition, molecular docking and enrichment analysis of the overlapping targets between lung cancer and ingredients were performed to investigate the potential mechanisms of ingredient candidates against lung cancer. Ten potential ingredients against lung cancer were identified and they may have similar effect on the development of lung cancer. The results obtained from this study offered valuable insights and provided potential avenues for the development of novel drugs aimed at treating lung cancer.

TRAUMATIC BRAIN INJURY AND ITS IMPLICATIONS FOR BEHAVIORAL HEALTH FACTORS.

The constant increase in the level of traumatic brain injuries in recent years, the frequent cases of disability and mortality associated with them require in-depth comprehensive research to study the problem on the ground, its medical, social, and economic aspects, which is very important for improving organizational measures to reduce traumatization among all age groups of the population. Objectives - to determine the presence and nature of structural damage associated with traumatic brain injury. The presence and nature of structural damage associated with traumatic brain injury. The studies included data on the treatment of victims with traumatic brain injuries from 2016 to 2020 on the basis of the Surgical Clinic of the Azerbaijan Medical University. Among the victims, men accounted for 77.9% and women 22.1%. In a prospective comparative study, after signing informed consent, 299 people of different sexes were included, of which 90 were victims with isolated TBI. The inclusion criteria for the study were as follows: victims with a verified diagnosis of TBI; age over 18; patients without concomitant somatic pathology. In a gender-comparative analysis of the revealed data, an injury combined with fractures of the bones of the extremities was recorded in 77 (81.1%) males and 18 of their female opponents, who also received TBI and accounted for 18.9%. Also high, especially in the male half of the examined injured persons, was the frequency of occurrence of TBI combinations with rib fractures and injuries of the chest organs, such injuries were registered in 41 victims, which accounted for 77.4% of all the above combined TBI. Somewhat less in both sex groups was TBI in combination with traumatic injuries of organs and tissues of the abdominal region, as well as with mixed injuries (χ2 criterion is 2.066; Df=4; p=0.724). The lowest level of TBI was observed in people under the age of 20 and older than 70 years, in other groups this figure increased sharply, reaching a maximum at the age of 20-29 and 40-49 years, and stabilized in the age groups over 49 years. The maximum number of cases associated with partial or complete loss of consciousness was recorded in persons aggravated by simultaneous traumatization of the upper or lower extremities and chest, as well as in isolated TBI.

Coarse-to-fine visual representation learning for medical images via class activation maps.

The value of coarsely labeled datasets in learning transferable representations for medical images is investigated in this work. Compared to fine labels which require meticulous effort to annotate, coarse labels can be acquired at a significantly lower cost and can provide useful training signals for data-hungry deep neural networks. We consider coarse labels in the form of binary labels differentiating a normal (healthy) image from an abnormal (diseased) image and propose CAMContrast, a two-stage representation learning framework for medical images. Using class activation maps, CAMContrast makes use of the binary labels to generate heatmaps as positive views for contrastive representation learning. Specifically, the learning objective is optimized to maximize the agreement within fixed crops of image-heatmap pair to learn fine-grained representations that are generalizable to different downstream tasks. We empirically validate the transfer learning performance of CAMContrast on several public datasets, covering classification and segmentation tasks on fundus photographs and chest X-ray images. The experimental results showed that our method outperforms other self-supervised and supervised pretrain methods in terms of data efficiency and downstream performance.

Enhancing medical image segmentation with a multi-transformer U-Net.

Various segmentation networks based on Swin Transformer have shown promise in medical segmentation tasks. Nonetheless, challenges such as lower accuracy and slower training convergence have persisted. To tackle these issues, we introduce a novel approach that combines the Swin Transformer and Deformable Transformer to enhance overall model performance. We leverage the Swin Transformer's window attention mechanism to capture local feature information and employ the Deformable Transformer to adjust sampling positions dynamically, accelerating model convergence and aligning it more closely with object shapes and sizes. By amalgamating both Transformer modules and incorporating additional skip connections to minimize information loss, our proposed model excels at rapidly and accurately segmenting CT or X-ray lung images. Experimental results demonstrate the remarkable, showcasing the significant prowess of our model. It surpasses the performance of the standalone Swin Transformer's Swin Unet and converges more rapidly under identical conditions, yielding accuracy improvements of 0.7% (resulting in 88.18%) and 2.7% (resulting in 98.01%) on the COVID-19 CT scan lesion segmentation dataset and Chest X-ray Masks and Labels dataset, respectively. This advancement has the potential to aid medical practitioners in early diagnosis and treatment decision-making.

Multi-scale and multi-view network for lung tumor segmentation.

Lung tumor segmentation in medical imaging is a critical step in the diagnosis and treatment planning for lung cancer. Accurate segmentation, however, is challenging due to the variability in tumor size, shape, and contrast against surrounding tissues. In this work, we present MSMV-Net, a novel deep learning architecture that integrates multi-scale multi-view (MSMV) learning modules and multi-scale uncertainty-based deep supervision (MUDS) for enhanced segmentation of lung tumors in computed tomography images. MSMV-Net capitalizes on the strengths of multi-view analysis and multi-scale feature extraction to address the limitations posed by small 3D lung tumors. The results indicate that MSMV-Net achieves state-of-the-art performance in lung tumor segmentation, recording a global Dice score of 55.60% on the LUNA dataset and 59.94% on the MSD dataset. Ablation studies conducted on the MSD dataset further validate that our method enhances segmentation accuracy.

CoGSPro-net:A graph neural network based on protein-protein interaction for classifying lung cancer-relatrd proteins.

This paper proposes a deep learning algorithm named CoGSPro for classifying lung cancer-related proteins. CoGSPro combines graph neural networks and attention mechanisms to extract key features from protein data and accurately classify proteins. It utilizes large-scale protein expression datasets to train and validate the model, enabling it to identify subtle patterns related to lung cancer. CoGSPro integrates protein-protein interaction network information to improve its predictive accuracy. The experimental results indicate that CoGSPro achieves cutting-edge performance, attaining an accuracy of 96.60% in the classification of lung cancer proteins, surpassing other baseline methods. Additionally, CoGSPro has uncovered new biomarkers for lung cancer, offering potential targets for early detection and treatment.

Maximum a posteriori detection of heartbeats from a chest-worn accelerometer.

Objective. Unobtrusive long-term monitoring of cardiac parameters is important in a wide variety of clinical applications, such as the assesment of acute illness severity and unobtrusive sleep monitoring. Here we determined the accuracy and robustness of heartbeat detection by an accelerometer worn on the chest.Approach. We performed overnight recordings in 147 individuals (69 female, 78 male) referred to two sleep centers. Two methods for heartbeat detection in the acceleration signal were compared: one previously described approach, based on local periodicity, and a novel extended method incorporating maximumaposterioriestimation and a Markov decision process to approach an optimal solution.Main results. The maximumaposterioriestimation significantly improved performance, with a mean absolute error for the estimation of inter-beat intervals of only 3.5 ms, and 95% limits of agreement of -1.7 to +1.0 beats per minute for heartrate measurement. Performance held during posture changes and was only weakly affected by the presence of sleep disorders and demographic factors.Significance. The new method may enable the use of a chest-worn accelerometer in a variety of applications such as ambulatory sleep staging and in-patient monitoring.

FLASH radiotherapy for the treatment of symptomatic bone metastases in the thorax (FAST-02): protocol for a prospective study of a novel radiotherapy approach.

BACKGROUND: FLASH therapy is a treatment technique in which radiation is delivered at ultra-high dose rates (≥ 40 Gy/s). The first-in-human FAST-01 clinical trial demonstrated the clinical feasibility of proton FLASH in the treatment of extremity bone metastases. The objectives of this investigation are to assess the toxicities of treatment and pain relief in study participants with painful thoracic bone metastases treated with FLASH radiotherapy, as well as workflow metrics in a clinical setting. METHODS: This single-arm clinical trial is being conducted under an FDA investigational device exemption (IDE) approved for 10 patients with 1-3 painful bone metastases in the thorax, excluding bone metastases in the spine. Treatment will be 8 Gy in a single fraction administered at ≥ 40 Gy/s on a FLASH-enabled proton therapy system delivering a single transmission proton beam. Primary study endpoints are efficacy (pain relief) and safety. Patient questionnaires evaluating pain flare at the treatment site will be completed for 10 consecutive days post-RT. Pain response and adverse events (AEs) will be evaluated on the day of treatment and on day 7, day 15, months 1, 2, 3, 6, 9, and 12, and every 6 months thereafter. The outcomes for clinical workflow feasibility are the occurrence of any device issues as well as time on the treatment table. DISCUSSION: This prospective clinical trial will provide clinical data for evaluating the efficacy and safety of proton FLASH for palliation of bony metastases in the thorax. Positive findings will support the further exploration of FLASH radiation for other clinical indications including patient populations treated with curative intent. REGISTRATION: ClinicalTrials.gov NCT05524064.

BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition.

Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset's quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.