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.

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.

Despite the great socioeconomic burden of stroke, there have been few reports of stroke statistics in Korea. In this scenario, the Epidemiologic Research Council of the Korean Stroke Society launched the “Stroke Statistics in Korea” project, aimed at writing a contemporary, comprehensive, and representative report on stroke epidemiology in Korea. This report contains general statistics of stroke, prevalence of behavioral and vascular risk factors, stroke characteristics, pre-hospital system of care, hospital management, quality of stroke care, and outcomes. In this report, we analyzed the most up-to-date and nationally representative databases, rather than performing a systematic review of existing evidence. In summary, one in 40 adults are patients with stroke and 232 subjects per 100,000 experience a stroke event every year. Among the 100 patients with stroke in 2014, 76 had ischemic stroke, 15 had intracerebral hemorrhage, and nine had subarachnoid hemorrhage. Stroke mortality is gradually declining, but it remains as high as 30 deaths per 100,000 individuals, with regional disparities. As for stroke risk factors, the prevalence of smoking is decreasing in men but not in women, and the prevalence of alcohol drinking is increasing in women but not in men. Population-attributable risk factors vary with age. Smoking plays a role in young-aged individuals, hypertension and diabetes in middle-aged individuals, and atrial fibrillation in the elderly. About four out of 10 hospitalized patients with stroke are visiting an emergency room within 3 hours of symptom onset, and only half use an ambulance. Regarding acute management, the proportion of patients with ischemic stroke receiving intravenous thrombolysis and endovascular treatment was 10.7% and 3.6%, respectively. Decompressive surgery was performed in 1.4% of patients with ischemic stroke and in 28.1% of those with intracerebral hemorrhage. The cumulative incidence of bleeding and fracture at 1 year after stroke was 8.9% and 4.7%, respectively. The direct costs of stroke were about ₩1.68 trillion (KRW), of which ₩1.11 trillion were for ischemic stroke and ₩540 billion for hemorrhagic stroke. The great burden of stroke in Korea can be reduced through more concentrated efforts to control major attributable risk factors for age and sex, reorganize emergency medical service systems to give patients with stroke more opportunities for reperfusion therapy, disseminate stroke unit care, and reduce regional disparities. We hope that this report can contribute to achieving these tasks.
Adult ; Aged ; Alcohol Drinking ; Ambulances ; Atrial Fibrillation ; Cerebral Hemorrhage ; Emergency Medical Services ; Emergency Service, Hospital ; Epidemiology ; Female ; Hemorrhage ; Hope ; Humans ; Hypertension ; Incidence ; Korea ; Male ; Mortality ; Prevalence ; Reperfusion ; Risk Factors ; Smoke ; Smoking ; Stroke ; Subarachnoid Hemorrhage ; Writing

Skull base chordomas are rare, malignant tumors arising from primitive notochord remnants of the axial skeleton and comprise approximately 25–35% of all chordoma cases. Nasal endoscopy in previous case reports has characterized nasopharyngeal chordomas as firm, semi-translucent masses protruding from the posterior nasopharyngeal wall with a pink, “meaty” appearance. However, the nasopharyngeal chordoma in the present case had a soft, cystic appearance, unlike the tumors previously described. Herein, an unusual case of an incidentally discovered nasopharyngeal chordoma is reported in a patient with papillary thyroid cancer; the discovered chordoma had a benign cystic appearance with no abnormal positron emission tomography-computed tomography (PET-CT) uptake.
Chordoma ; Cranial Fossa, Posterior ; Electrons ; Endoscopy ; Humans ; Notochord ; Skeleton ; Skull Base ; Thyroid Gland ; Thyroid Neoplasms