Solitary fibrous tumors (SFT) are rare fibroblastic mesenchymal neoplasms which are originally described as neoplasms of the pleura originating from the spindle cells. Although it can originate from extrapleural sites including the head and neck, it is exceedingly rare in the sinonasal tract. There has been no reported cases of SFT involving the paranasal sinuses in Korea; however, there was case of a 34-year-old man who presented with persistent left nasal obstruction and watering of the left eye. Imaging by CT and MRI revealed a large, highly vascular tumor occupying the maxilloethmoidal sinus cavities associated with bony wall destruction, masquerading as maxillary sinus cancer. The tumor mass occupying sinus cavities was removed through endoscopic and Caldwell-Luc approach. Histopathological examination of the tumor was consistent with SFT. We report this case to further insights regarding the diagnosis and management of this rare tumor.

PURPOSE: Homeless patients usually live outside and are therefore frequently exposed to injury and tetanus infection. Thus, after visits to an emergency department (ED) due to injury, homeless patients need to be vaccinated for the prevention of tetanus infection with tetanus immunoglobulin regardless of tetanus antibody titer or previous vaccination history. Because the exact history of previous tetanus vaccination in homeless patients is unclear, the tetanus antibody titer between homeless patients and general patients was assessed. METHODS: Subjects who visited the ED after injury from October 2008 to February 2010 were enrolled. All participants answered questions on age, gender, previous vaccination or prophylaxis history, and military service. The Tetanus Immunoglobulin G ELISA (Enzyme-linked immunosorbent assay) method was used for the analysis of serum samples. Propensity score-matched analysis was used to control for age, gender, previous vaccination or prophylaxis history, and military service. RESULTS: A total of 1325 samples were analyzed. There was 83 samples from homeless patients and 1242 samples from general patients. After matched analysis using the propensity score, 56 subjects were matched. The geometric mean titer of tetanus antibody was 0.204+/-0.392 IU/mL in homeless patients and 0.105+/-0.143 IU/mL in general patients (p=0.078). The proportion of patients with a safe tetanus antibody titer was 66.1 percent of homeless patients and 23.2 percent of general patients (p<0.001). CONCLUSION: Homeless patients had a higher mean titer and a statistically higher proportion had a safe titer compared to general patients.
Emergencies ; Enzyme-Linked Immunosorbent Assay ; Humans ; Immunoglobulin G ; Immunoglobulins ; Methods ; Military Personnel ; Propensity Score ; Tetanus* ; Vaccination

PURPOSE: Homeless patients usually live outside and are therefore frequently exposed to injury and tetanus infection. Thus, after visits to an emergency department (ED) due to injury, homeless patients need to be vaccinated for the prevention of tetanus infection with tetanus immunoglobulin regardless of tetanus antibody titer or previous vaccination history. Because the exact history of previous tetanus vaccination in homeless patients is unclear, the tetanus antibody titer between homeless patients and general patients was assessed. METHODS: Subjects who visited the ED after injury from October 2008 to February 2010 were enrolled. All participants answered questions on age, gender, previous vaccination or prophylaxis history, and military service. The Tetanus Immunoglobulin G ELISA (Enzyme-linked immunosorbent assay) method was used for the analysis of serum samples. Propensity score-matched analysis was used to control for age, gender, previous vaccination or prophylaxis history, and military service. RESULTS: A total of 1325 samples were analyzed. There was 83 samples from homeless patients and 1242 samples from general patients. After matched analysis using the propensity score, 56 subjects were matched. The geometric mean titer of tetanus antibody was 0.204+/-0.392 IU/mL in homeless patients and 0.105+/-0.143 IU/mL in general patients (p=0.078). The proportion of patients with a safe tetanus antibody titer was 66.1 percent of homeless patients and 23.2 percent of general patients (p<0.001). CONCLUSION: Homeless patients had a higher mean titer and a statistically higher proportion had a safe titer compared to general patients.
Emergencies ; Enzyme-Linked Immunosorbent Assay ; Humans ; Immunoglobulin G ; Immunoglobulins ; Methods ; Military Personnel ; Propensity Score ; Tetanus* ; Vaccination

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.