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Purpose: Estrogen receptor-a (ER-a) and-h (ER-h) play important roles in the carcinogenesis of breast tumors. Similarly, there have been several reports of ER expression in lung cancers, but the results have not been consistent, and the receptors' prognostic value remains unclear. Our goal was to investigate ER expression in non^small cell lung cancer (NSCLC) and to assess whether their expression correlates with prognosis. Experimental Design: ER expression was examined using immunohistochemical methods with sections from 132 resected NSCLC specimens. Kaplan-Meier survival curves were analyzed to determine the significance of ER expression in the prognosis of NSCLC patients. Results: ER-a was detected in the cytoplasm of 73% of the specimens analyzed, whereas ER-h was detected in the nucleus of 51%. ER-a expression correlated with poorer overall survival (P < 0.001), as did the absence of ER-h expression (P = 0.048). Likewise, at histopathologic stage I, ER-a expression (P = 0.028) or the absence of ER-h (P = 0.

Background and Purpose— The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods. Methods— The derivation cohort included 387 LVO patients, and the external validation cohort included 115 LVO patients with anterior circulation who were treated with mechanical thrombectomy. The statistical model with logistic regression without regularization and machine learning algorithms, such as regularized logistic regression, linear support vector machine, and random forest, were used to predict good clinical outcome (modified Rankin Scale score of 0–2 at 90 days) with standard and multiple pretreatment clinical variables. Five previously reported pretreatment scoring methods (the Pittsburgh Response to Endovascular Therapy score, the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale index, the Totaled Health Risks in Vascular Events score, the Houston Intra-Arterial Therapy score, and the Houston Intra-Arterial Therapy 2 score) were compared with these models for the area under the receiver operating characteristic curve. Results— The area under the receiver operating characteristic curve of random forest, which was the worst among the machine learning algorithms, was significantly higher than those of the standard statistical model and the best model among the previously reported pretreatment scoring methods in the derivation (the area under the receiver operating characteristic curve were 0.85±0.07 for random forest, 0.78±0.08 for logistic regression without regularization, and 0.77±0.09 for Stroke Prognostication using Age and National Institutes of Health Stroke Scale) and validation cohorts (the area under the receiver operating characteristic curve were 0.87±0.01 for random forest, 0.56±0.07 for logistic regression without regularization, and 0.83±0.00 for Pittsburgh Response to Endovascular Therapy). Conclusions— Machine learning methods with multiple pretreatment clinical variables can predict clinical outcomes of patients with anterior circulation LVO who undergo mechanical thrombectomy more accurately than previously developed pretreatment scoring methods.

SummaryBackground and objectives The association between mortality and physical activity based on self-report questionnaire in hemodialysis patients has been reported previously. However, because self-report is a subjective assessment, evaluating true physical activity is difficult. This study investigated the prognostic significance of habitual physical activity on 7-year survival in a cohort of clinically stable and adequately dialyzed patients.Design, setting, participants, & measurements A total of 202 Japanese outpatients who were undergoing maintenance hemodialysis three times per week at the hemodialysis center of Sagami Junkanki Clinic (Japan) from October 2002 to February 2012 were followed for up to 7 years. Physical activity was evaluated using an accelerometer at study entry and is expressed as the amount of time a patient engaged in physical activity on nondialysis days. Cox proportional hazard regression was used to assess the contribution of habitual physical activity to all-cause mortality.Results The median patient age was 64 (25th, 75th percentiles, 57, 72) years, 52.0% of the patients were women, and the median time on hemodialysis was 40.0 (25th, 75th percentiles, 16.8, 119.3) months at baseline. During a median follow-up of 45 months, 34 patients died. On multivariable analysis, the hazard ratio for all-cause mortality per 10 min/d increase in physical activity was 0.78 (95% confidence interval, 0.66-0.92; P=0.002).Conclusions Engaging in habitual physical activity among outpatients undergoing maintenance hemodialysis was associated with decreased mortality risk.