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BACKGROUND Imperforate hymen is a rare obstructive anomaly of the female reproductive tract. It is associated with complications, such as cyclical abdominal pain, urinary retention, and pelvic mass. CASE SUMMARY A 13-year-old girl presented several times to the emergency room with lower abdominal pain for a year. She received conservative treatment, such as pain control, at each visit. She visited our gynecological clinic for worsening pain, and a 14-cm hematocolpos was found on ultrasonography. She was finally diagnosed with an imperforate hymen with hematocolpometra. Hymenectomy was performed, which resulted in event-free regular cyclical menstruation. CONCLUSION Imperforate hymen should be considered in a premenarcheal adolescent girl with periodic abdominal pain.

Background To evaluate the clinical outcome of atypical endometriosis and its association with ovarian malignancy. Methods This retrospective study included patients diagnosed with atypical endometriosis between January 2001 and December 2017. All patients had received surgical treatment for ovarian tumor. The clinical characteristics and histopathological results of all patients were reviewed. Results Atypical endometriosis was diagnosed in 101 patients. We analyzed 98 patients with a mean age of 34.8 years (range: 16–58 years). Ten patients (10.2%) had previously undergone endometriosis surgery more than once. In total, 12 (12.2%) patients had atypical endometriosis-associated ovarian malignancy—nine had carcinomas and three had borderline tumor. The tumors were pathologically classified as follows: five, clear cell carcinomas; two, endometrioid adenocarcinomas; one, mixed clear cell and endometrioid adenocarcinoma; one, seromucinous carcinoma; two, mucinous borderline tumors; and one, seromucinous borderline tumor. Conclusion Atypical endometriosis is most frequently associated with clear cell carcinoma and endometrioid adenocarcinoma. To identify the risk of ovarian malignancy and manage patients with endometriosis, diagnosing atypical endometriosis and recognizing its precancerous potential are important.

This study aimed to evaluate the incidence, clinical diagnosis, surgical treatment, and histopathological findings of adnexal masses in children and adolescents. This retrospective study included patients aged

Introduction: A significant portion of patients with acute minor stroke have poor functional outcome due to early neurological deterioration (END). The purpose of this study is to investigate the applicability of machine learning algorithms to predict END in patients with acute minor stroke. Methods: We collected clinical and neuroimaging information from patients with acute minor stroke with NIHHS score of 3 or less. Early neurological deterioration was defined as any worsening of NIHSS score within three days after admission. Poor functional outcome was defined as a modified Rankin Scale score of 2 or more. We also compared clinical and neuroimaging information between END and No END group. Four machine learning algorithms, i.e., Boosted trees, Bootstrap decision forest, Deep learning, and Logistic Regression, are selected and trained by our dataset to predict early neurological deterioration Results: A total of 739 patients were included in this study. Seventy-eight patients (10.6%) had early neurological deterioration. Among 78 patients with END, 61 (78.2%) had poor functional outcomes at 90 days after stroke onset. On multivariate analysis, NIHSS score on admission (P=0.003), hemorrhagic transformation(P=0.010), and stenosis (P=0.014) or occlusion (P=0.004) of a relevant artery were independently associated with END. Compared with four machine learning algorithms, Boosted trees, Deep learning, and Logistic Regression achieved an excellent prediction of END in patients with acute minor stroke (Boosted trees: accuracy = 0.966, F1 score = 0.8 and an area under the curve value = 0.934, Deep learning :0.966, 0.8, 0. 904, and Logistic Regression : 0.966, 0.8, 0.885). Conclusions: This study suggests that machine learning algorithms which integrate clinical and neuroimaging information accurately predict END in patients with acute minor ischemic stroke. Further studies based on an extensive data set are needed to predict END accurately for treatment strategies and better functional outcome.

Introduction: Early neurological deterioration is one of the critical determinants of functional outcomes in patients with minor ischemic stroke. The purpose of this study was to identify predictors of early neurological deterioration in patients with acute minor ischemic stroke. Methods: A total of 739 patients with acute minor ischemic stroke who are admitted within 24 hours after onset of stroke symptom between January 2014 and December 2018 were enrolled in this study. We analyzed demographic characteristics, risk factors for vascular diseases, stroke severity, stroke subtypes, neuroimaging parameters, and relevant arterial steno-occlusive lesions. Early neurological deterioration was defined as any worsening of neurological deficits within 3 days after admission. Logistic regression was used to determine independent predictors of early neurological deterioration. Results: Seventy-eight of 739 (10.5%) patients had early neurological deterioration. Among 78 patients with early neurological deterioration, 61 (78.2%) had poor functional outcomes at 90 days after stroke onset. By contrast, 131 of 661 (19.8%) patients without early neurological deterioration had poor functional outcomes. Multivariate analysis identified hemorrhagic transformation (OR, 3.8; 95% CI, 1.4-10.5; P=0.010), higher score of NIHSS on admission (OR, 1.4; 95% CI, 1.1-1.7; P=0.003), relevant arterial stenosis (OR, 2.0; 95% CI, 1.2-3.5; p=0.014) and occlusion (OR, 2.6; 95% CI, 1.4-4.8; p=0.004) were the factors associated with early neurological deterioration. Conclusions: The results of this study suggest that hemorrhagic transformation, higher NIHSS score on admission, relevant arterial steno-occlusive lesions are independent predictors of early neurological deterioration in patients with acute minor ischemic stroke.

Objectives A significant proportion of patients with acute minor stroke have unfavorable functional outcome due to early neurological deterioration (END). The purpose of this study was to evaluate the applicability of machine learning algorithms to predict END in patients with acute minor stroke. Patients and methods We collected clinical and neuroimaging information from patients with acute minor stroke with NIHSS score of ≤ 3. Early neurological deterioration was defined as any worsening of NIHSS score within 3 days after admission. Unfavorable functional outcome was defined as a modified Rankin Scale score of ≥ 2. We also compared clinical and neuroimaging information between patients with and without END. Four machine learning algorithms, i.e., Boosted trees, Bootstrap decision forest, Deep neural network, and Logistic Regression, were selected and trained by our dataset to predict early neurological deterioration Results A total of 739 patients were included in this study. 78 patients (10.6%) experienced END. Among 78 patients with END, 61 (78.2%) had unfavorable functional outcome at 90 days after stroke onset. On multivariate analysis, the initial NIHSS score (P = 0.003), hemorrhagic transformation (P = 0.010), and stenosis (P = 0.014) or occlusion (P = 0.004) of a relevant artery were independently associated with END. Of the four machine learning algorithms, Boosted trees, Deep neural network, and Logistic Regression can be used to predict END in patients with acute minor stroke (Boosted trees: accuracy = 0.966, F1 score = 0.8 and area under the curve = 0.934, Deep neural network :0.966, 0.8, and 0. 904, and Logistic Regression : 0.966, 0.8, and 0.885). Conclusions This study suggests that machine learning algorithms that integrate clinical and neuroimaging information can be used to predict END in patients with acute minor stroke. Further studies based on larger, multicenter datasets are needed to predict END accurately for designing treatment strategies and obtaining favorable functional outcome.