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PurposePatients with gastric cancer after gastrectomy often suffer from a decline in their quality of life (QoL), but the relationship between body composition (BC) and physical function on QoL has rarely been studied. This study aims to evaluate and determine the changes in QoL after gastrectomy and the impact of BC and physical function on QoL.MethodsA total of 311 gastric cancer patients completed EORTC QLQ-C30 and EORTC QLQ-STO22 questionnaires before and 1, 3, 6 months post-surgery. Data including BC, handgrip strength (HGS) and 6-m gait speed (GS) were collected prospectively. Multiple linear regression analysis was used to determine the correlation between QoL and BC, HGS and GS.ResultsPatients had significantly worse scores after surgery on most function and symptom scales (p < 0.001), but most of these scales recovered within 6 months after surgery. A higher subcutaneous fat area (SFA)was associated with increased symptom scores 1 month after surgery. A higher GS is associated with a better global health status symptom.ConclusionPatients suffer from a decline in their QoL after gastrectomy for gastric cancer. Intervention strategies aiming at reducing SFA and improving GS may improve the QoL in patients underwent gastrectomy for gastric cancer.

Objective: To detect the abdomen fat area of ​​people in Harbin area by quantitative CT, and analyze its correlation with blood lipids and blood sugar levels. Methods: A total of 411 patients over 20 years old were selected in the physical examination center of the First Affiliated Hospital of Harbin Medical University, aged 23-90 years old, with a median age of 58 years old. Physical examination and biochemical index determination were carried out, and patients were divided into normal group, overweight group and obese group according to the body mass index (BMI) value, and quantitative CT (quantitative CT, QCT) fat measurement software was used to quantitatively measure the patient's L2 ～3 levels of abdominal visceral fat area (VFA) and subcutaneous fat area (SFA). Group the VFA and SFA data, compare the difference of VFA value and SFA value between each group, and carry out correlation analysis. Results: The VFA value (P=0.000, 0.000, 0.033) and SFA value (P=0.000, 0.000, 0.000) of the three groups were significantly different (all P<0.05), all of them were obese group>overweight>normal group . VFA values ​​are significantly correlated with blood lipids and blood glucose (the r values ​​of VFA and TG are 0.466, 0.15, and 0.318, respectively, and the P values ​​are 0, 0.042, and 0.033, respectively; the r values ​​of VFA and FBG are 0.357, 0.171, and 0.417, respectively., P values ​​are 0, 0.02, 0.004). Diagnostic drawing VFA triglyceride (TG) and fasting blood glucose (FBG) values of ROC curve abnormal, respectively, when VFA cm & lt 166.035 2 and 172.65cm 2, the diagnosis TG, FBG abnormal sensitivity and the highest specificity. Conclusion: There is a significant difference in abdominal fat area between normal and abnormal BMI groups, and the increase of VFA will increase the risk of hyperlipidemia and hyperglycemia. VFA has important application value for evaluating whether TG and FBG values ​​are normal. Objective: To detect the abdomen fat area of ​​people in Harbin area by quantitative CT, and analyze its correlation with blood lipids and blood sugar levels. Methods: A total of 411 patients over 20 years old were selected in the physical examination center of the First Affiliated Hospital of Harbin Medical University, aged 23-90 years old, with a median age of 58 years old. Physical examination and biochemical index determination were carried out, and patients were divided into normal group, overweight group and obese group according to the body mass index (BMI) value, and quantitative CT (quantitative CT, QCT) fat measurement software was used to quantitatively measure the patient's L2 ～3 levels of abdominal visceral fat area (VFA) and subcutaneous fat area (SFA). Group the VFA and SFA data, compare the difference of VFA value and SFA value between each group, and carry out correlation analysis. Results: The VFA value (P=0.000, 0.000, 0.033) and SFA value (P=0.000, 0.000, 0.000) of the three groups were significantly different (all P<0.05), all of them were obese group>overweight>normal group . VFA values ​​are significantly correlated with blood lipids and blood glucose (the r values ​​of VFA and TG are 0.466, 0.15, and 0.318, respectively, and the P values ​​are 0, 0.042, and 0.033, respectively; the r values ​​of VFA and FBG are 0.357, 0.171, and 0.417, respectively., P values ​​are 0, 0.02, 0.004). Diagnostic drawing VFA triglyceride (TG) and fasting blood glucose (FBG) values of ROC curve abnormal, respectively, when VFA cm & lt 166.035 2 and 172.65cm 2, the diagnosis TG, FBG abnormal sensitivity and the highest specificity. Conclusion: There is a significant difference in abdominal fat area between normal and abnormal BMI groups, and the increase of VFA will increase the risk of hyperlipidemia and hyperglycemia. VFA has important application value for evaluating whether TG and FBG values ​​are normal. [ABSTRACT FROM AUTHOR]

Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estimate size of fat areas, this study aims to develop and test a computer-aided detection (CAD) scheme based on deep learning technique to automatically segment subcutaneous fat areas (SFA) and visceral fat areas (VFA) depicting on volumetric CT images. A retrospectively collected CT image dataset was divided into two independent training and testing groups. The proposed CAD framework consisted of two steps with two convolution neural networks (CNNs) namely, Selection-CNN and Segmentation-CNN. The first CNN was trained using 2,240 CT slices to select abdominal CT slices depicting SFA and VFA. The second CNN was trained with 84,000 pixel patches and applied to the selected CT slices to identify fat-related pixels and assign them into SFA and VFA classes. Comparing to the manual CT slice selection and fat pixel segmentation results, the accuracy of CT slice selection using the Selection-CNN yielded 95.8%, while the accuracy of fat pixel segmentation using the Segmentation-CNN was 96.8%. This study demonstrated the feasibility of applying a new deep learning based CAD scheme to automatically recognize abdominal section of human body from CT scans and segment SFA and VFA from volumetric CT data with high accuracy or agreement with the manual segmentation results. [ABSTRACT FROM AUTHOR]