Objective To review and report diseases of the sphenoid sinus from the literature and from a university hospital. Methods Inpatients' data were retrospectively gathered and reviewed from January 2006 to June 2016. Clinical data, imaging, organisms, and pathological reports were collected. Pathology was divided into infection/inflammation, tumor, and miscellaneous. A literature review was performed with the search term “isolated sphenoid disease” in PubMed. Original primary studies with 20 patients or more were reviewed. Results and Discussion One hundred and twenty-two patients were enrolled. Seventy-two subjects were female (59%). The average age was 54.3 years (±18.0). Imaging abnormalities were found incidentally in 27 patients (22.1%). The most common symptom was headache (63.9%). Visual loss, the second most common symptom, was more frequent in the tumor group (30.6% versus 54.2%). From the literature review, 21 primary studies with 1,320 total patients were included. From all studies and the present study, infection/inflammation was the most common pathology (75%) [95% confidence interval (CI): 0.696, 0.804]. Overall, tumors were found in 18.9% and malignant tumors in 7.0% [95% CI: 0.045, 0.095]. Conclusion A specific diagnosis of a sphenoid lesion is needed during active investigation. Infection/inflammation was the most common pathology and malignancy was found in 7%.

Traumatic Brain Injury (TBI) could lead to intracranial hemorrhage (ICH), which has now been identified as a major cause of death after trauma if it is not adequately diagnosed and properly treated within the first 24 hours. CT examination is widely preferred for urgent ICH diagnosis, which enables the fast identification and detection of ICH regions. However, the use of it requires the clinical interpretation by experts to identify the subtypes of ICH. Besides, it is unable to provide the details needed to conduct quantitative assessment, such as the volume and thickness of hemorrhagic lesions, which may have prognostic importance to the decision-making on emergency treatment. In this paper, an optimal deep learning framework is proposed to assist the quantitative assessment for ICH diagnosis and the accurate detection of different subtypes of ICH through head CT scan. Firstly, the format of raw input data is converted from 3D DICOM to NIfTI. Secondly, a pre-trained multi-class semantic segmentation model is applied to each slice of CT images, so as to obtain a precise 3D mask of the whole ICH region. Thirdly, a fine-tuned classification neural network is employed to extract the key features from the raw input data and identify the subtypes of ICH. Finally, a quantitative assessment algorithm is adopted to automatically measure both thickness and volume via the 3D shape mask combined with the output probabilities of the classification network. The results of our extensive experiments demonstrate the effectiveness of the proposed framework where the average accuracy of 96.21 percent is achieved for three types of hemorrhage. The capability of our optimal classification model to distinguish between different types of lesion plays a significant role in reducing the false-positive rate in the existing work. Furthermore, the results suggest that our automatic quantitative assessment algorithm is effective in providing clinically relevant quantification in terms of volume and thickness. It is more important than the qualitative assessment conducted through visual inspection to the decision-making on emergency surgical treatment.

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BACKGROUND AND PURPOSE: Facial parameters are used for evaluating normal growth patterns, diagnosing patients with craniofacial abnormalities, and planning surgical procedures. However, these parameters vary by ethnicity and race. This study aims to describe soft-tissue and bony facial parameters based on CT of healthy pediatric and adolescent patients in Thailand. MATERIALS AND METHODS: CT imaging of the brain, orbit, facial bones, and neck was performed at Maharaj Nakorn Chiangmai Hospital, in patients from birth to 19 years old. Patients with known syndromic disease, craniofacial syndrome, facial trauma and/or infection, and previous surgery that deformed the study area were excluded. The key points of measurement were soft-tissue intercanthal, bony interorbital, and bony lateral orbital distances. RESULTS: There were 932 patients: 554 males (59.4%) and 378 females (40.6%). Facial parameters rapidly increased in the first 2 years of life. Significant differences in these parameters between the males and females were found at the age of $15 years. However, ratios of the interorbital to the lateral orbital distance were generally consistent among age groups in both sexes, at 0.25. CONCLUSIONS: This study, in Thailand, provides detailed age-and sex-specific normative data of the craniofacial measurements in children and adolescences based on CT imaging. These data can be used for evaluating individual patients with craniofacial abnormalities as well as determining the treatment in Thai and Asian populations, in whom craniofacial abnormalities, for example, frontoethmoidal encephalomeningocele, are common. ABBREVIATIONS: IC ¼ intercanthal; IO ¼ interorbital; LO ¼ lateral orbital; SD ¼ standard deviation F acial parameters and proportions play important roles in medicine because they are used for evaluating normal growth patterns, diagnosing patients with craniofacial abnormalities, and planning surgical procedures. 1,2 One of the most common craniofacial abnormalities in southeast Asia is frontoethmoidal encephalomeningocele, 3 a congenital pediatric disorder characterized by herniation of the brain and the meninges through an anterior skull defect that usually involves the orbits. 3-5 Although rare in Europe and the United States, it has been described in several pediatric groups of different races in southeast Asia, including Thais, Malaysians, and Burmese. 3 The incidence of frontoethmoidal

Introduction: Diagnosis of emergent intracranial lesions that require emergency treatment either medically or surgically in non-traumatic pediatric headaches is important. Red-flag signs and symptoms are commonly used as justification for neuroimaging; however, evidence on its diagnostic values is limited. The study aims to identify diagnostic values of red-flags and develop a clinical prediction score to help improve the diagnostic yield of neuroimaging. Methods: A retrospective review of 109 pediatric patients from 2006 to 2020 who presented with a non-traumatic headache was conducted. A clinical prediction score from red flags was developed using multivariate logistic regression. Discriminatory ability was examined using the area under the receiver operating characteristic curve. Results: A total of 51 patients were diagnosed with emergent intracranial lesions. Four potential clinical red flag predictors were identified: (1) acute onset (less than 3 months), (2) altered conscious state, (3) focal motor abnormality, and (4) and ocular/pupillary abnormality or squint. A clinical prediction score was developed with good discriminatory properties (0.84). Conclusions: Clinical predictor scores from these four red flags may play an important role in maximizing neuroimaging and proper management for pediatric patients with non-traumatic headaches. Future validation studies are needed and could guide referrals and optimize the use of neuroimaging for these patients.

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