Purpose:To evaluate the role of diffusion-weighted imaging (DWI) in the detection of breast cancers, and to correlate the apparent diffusion coefficient (ADC) value with prognostic factors. Materials and Methods:Sixty-seven women with invasive cancer underwent breast MRI. Histological specimens were analyzed for tumor size and grade, and expression of estrogen receptors (ER), progesterone receptors, c-erbB-2, p53, Ki-67, and epidermal growth factor receptors. The computed mean ADC values of breast cancer and normal breast parenchyma were compared. Relationships between the ADC values and prognostic factors were determined using Wilcoxon signed rank test and Kruskal-Wallis test.Results: DWI detected breast cancer as a hyperintense area in 62 patients (92.5 %). A statistically significant difference in the mean ADC values of breast cancer (1.09 Ϯ 0.27 ϫ 10 Ϫ5 mm 2 /s) and normal parenchyma (1.59 Ϯ 0.27 ϫ 10 Ϫ5 mm 2 /s) was detected (P Ͻ 0.0001). There were no correlations between the ADC value and prognostic factors. However, the median ADC value was lower in the ER-positive group than the ER negative group, and this difference was marginally significant (1.09 ϫ 10 Ϫ5 mm 2 /s versus 1.15 ϫ 10 Ϫ5 mm 2 /s, P ϭ 0.053). Conclusion:The ADC value was a helpful parameter in detecting malignant breast tumors, but ADC value could not predict patient prognosis. DYNAMIC CONTRAST MATERIAL-enhanced MRI, which gives information on morphology and kinetics and has higher sensitivity, is frequently used to identify additional lesions and to determine the extent of tumor before surgery (1). In recent years, some studies have attempted to differentiate between benign and malignant tumors using diffusion-weighted imaging (DWI) (2-9). DWI is a specific modality that visualizes the microstructural characteristics of water diffusion in biological tissues. The microscopic motion includes the molecular diffusion of water and blood microcirculation in capillary networks; therefore, both diffusion and perfusion affect apparent diffusion coefficient (ADC) values (2,10). The ADC value was determined to be lower in cancer compared with normal parenchyme or benign breast tumor (2-9). Many studies have attempted to predict treatment response and prognosis in patients with breast cancer. It has been disclosed that there are traditional prognostic factors such as tumor grade and molecular markers such as estrogen receptors (ER) and progesterone receptors (PR) (11). Of these prognostic factors, the histologic grade of the tumor and the Ki-67 proliferation index reflect the cellularity (5,8,12,13), and c-erbB-2 and ER are thought to be associated with perfusion (14,15). We have speculated that these prognostic factors can affect the ADC value.To our knowledge, no studies have examined the correlation between the ADC value and prognostic factors. The objectives of the current study are to examine the clinical usefulness of DWI for the detection of invasive cancer, and to determine whether the ADC value can be a new prognostic factor for patients with...

BackgroundBlood pressure (BP) is directly and causally associated with body size in the general population. Whether muscle mass is an important factor that determines BP remains unclear.ObjectiveTo investigate whether sarcopenia is associated with hypertension in older Koreans.ParticipantsWe surveyed 2,099 males and 2,747 females aged 60 years or older.MeasurementsSarcopenia was defined as an appendicular skeletal muscle mass divided by body weight (ASM/Wt) that was <1 SD below the gender-specific mean for young adults. Obesity was defined as a body mass index (BMI) ≥25 kg/m2. Subjects were divided into four groups based on presence or absence of obesity or sarcopenia. Hypertension was defined as a systolic BP (SBP) ≥140 mmHg, a diastolic BP (DBP) ≥90 mmHg, or a self-reported current use of antihypertensive medications.ResultsThe overall prevalence of hypertension in the four groups was as follows 49.7% for non-obese non-sarcopenia, 60.9% for non-obese sarcopenia, 66.2% for obese non-sarcopenia and 74.7% for obese sarcopenia. After adjustment for age, gender, regular activity, current smoking and alcohol use, the odds ratio (OR) for having hypertension was 1.5 (95% confidence interval (CI) = 1.23–1.84) in subjects in the non-obese sarcopenia group, 2.08 (95% CI = 1.68–2.57) in the obese non-sarcopenia group and 3.0 (95% CI = 2.48–3.63) in the obese sarcopenia group, compared with the non-obese non-sarcopenia group (p for trend <0.001). Controlling further for body weight and waist circumference did not change the association between hypertension and sarcopenia. The association between sarcopenia and hypertension was more robust in the subjects with diabetes mellitus.ConclusionBody composition beyond BMI has a considerable impact on hypertension in elderly Koreans. Subjects with sarcopenic obesity appear to have a greater risk of hypertension than simply obese or sarcopenia subjects.

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BackgroundThe association of metabolic syndrome (MetS) with the development of Parkinson disease (PD) is currently unclear. We sought to determine whether MetS and its components are associated with the risk of incident PD using large-scale cohort data for the whole South Korean population.Methods and findingsHealth checkup data of 17,163,560 individuals aged ≥40 years provided by the National Health Insurance Service (NHIS) of South Korea between January 1, 2009, and December 31, 2012, were included, and participants were followed up until December 31, 2015. The mean follow-up duration was 5.3 years. The hazard ratio (HR) and 95% confidence interval (CI) of PD were estimated using a Cox proportional hazards model adjusted for potential confounders. We identified 44,205 incident PD cases during follow-up. Individuals with MetS (n = 5,848,508) showed an increased risk of PD development compared with individuals without MetS (n = 11,315,052), even after adjusting for potential confounders including age, sex, smoking, alcohol consumption, physical activity, income, body mass index, estimated glomerular filtration rate, and history of stroke (model 3; HR, 95% CI: 1.24, 1.21–1.27). Each MetS component was positively associated with PD risk (HR, 95% CI: 1.13, 1.10–1.16 for abdominal obesity; 1.13, 1.10–1.15 for hypertriglyceridemia; 1.23, 1.20–1.25 for low high-density lipoprotein cholesterol; 1.05, 1.03–1.08 for high blood pressure; 1.21, 1.18–1.23 for hyperglycemia). PD incidence positively correlated with the number of MetS components (log-rank p < 0.001), and we observed a gradual increase in the HR for incident PD with increasing number of components (p < 0.001). A significant interaction between age and MetS on the risk of incident PD was observed (p for interaction < 0.001), and people aged ≥65 years old with MetS showed the highest HR of incident PD of all subgroups compared to those <65 years old without MetS (reference subgroup). Limitations of this study include the possibilities of misdiagnosis of PD and reverse causality.ConclusionsOur population-based large-scale cohort study suggests that MetS and its components may be risk factors of PD development.

Recommending a point-of-interest (POI) a user will visit next based on temporal and spatial context information is an important task in mobile-based applications. Recently, several POI recommendation models based on conventional sequential-data modeling approaches have been proposed. However, such models focus on only a user's check-in sequence information and the physical distance between POIs. Furthermore, they do not utilize the characteristics of POIs or the relationships between POIs. To address this problem, we propose CAPE, the first content-aware POI embedding model which utilizes text content that provides information about the characteristics of a POI. CAPE consists of a check-in context layer and a text content layer. The check-in context layer captures the geographical influence of POIs from the check-in sequence of a user, while the text content layer captures the characteristics of POIs from the text content. To validate the efficacy of CAPE, we constructed a large-scale POI dataset. In the experimental evaluation, we show that the performance of the existing POI recommendation models can be significantly improved by simply applying CAPE to the models.