RESUMO
Pancreatic volume and fat fraction are critical prognoses for metabolic diseases like type 2 diabetes (T2D). Magnetic Resonance Imaging (MRI) is a required non-invasive quantification method for the pancreatic fat fraction. The dramatic development of deep learning has enabled the automatic measurement of MR images. Therefore, based on MRI, we intend to develop a deep convolutional neural network (DCNN) that can accurately segment and measure pancreatic volume and fat fraction. This retrospective study involved abdominal MR images from 148 diabetic patients and 246 healthy normoglycemic participants. We randomly separated them into training and testing sets according to the proportion of 80:20. There were 2364 recognizable pancreas images labeled and pre-treated by an upgraded superpixel algorithm for a discernible pancreatic boundary. We then applied them to the novel DCNN model, mimicking the most accurate and latest manual pancreatic segmentation process. Fat phantom and erosion algorithms were employed to increase the accuracy. The results were evaluated by dice similarity coefficient (DSC). External validation datasets included 240 MR images from 10 additional patients. We assessed the pancreas and pancreatic fat volume using the DCNN and compared them with those of specialists. This DCNN employed the cutting-edge idea of manual pancreas segmentation and achieved the highest DSC (91.2%) compared with any reported models. It is the first framework to measure intra-pancreatic fat volume and fat deposition. Performance validation reflected by regression R2 value between manual operation and trained DCNN segmentation on the pancreas and pancreatic fat volume were 0.9764 and 0.9675, respectively. The performance of the novel DCNN enables accurate pancreas segmentation, pancreatic fat volume, fraction measurement, and calculation. It achieves the same segmentation level of experts. With further training, it may well surpass any expert and provide accurate measurements, which may have significant clinical relevance.
RESUMO
In the overall management of the most chronic diseases, including diabetes mellitus (DM), adherence to recommended disease-related self-care activities is of paramount importance. The diagnosis and presence of a chronic disease may be considered a difficult and stressful situation in life, a situation in which coping mechanisms are psychological processes developed at a conscious level to manage these situations. This study aimed to explore the possible relationship between the dominance of one of the four major coping styles and adherence to diabetes-related self-care activities (DRSCAs) in the population of patients with type 2 DM (T2DM). In a cross-sectional consecutive-case population-based study design, 126 patients previously diagnosed with T2DM were enrolled. Coping mechanisms were evaluated using the Cope scale inventory, which identifies the dominant coping mechanism: problem-, emotion-, social support-, or avoidance-focused. The quality of DRSCA was evaluated using the summary of diabetes self-care activities questionnaire, in which a higher score was associated with improved adherence. In the study cohort, 45 patients (35.7%) had problem-focused coping, 37 (29.4%) had emotion-focused coping, 32 (25.4%) social support-focused coping, and 12 (9.5%) had avoidance-focused coping. Patients with emotion-focused coping had the highest level (P=0.02) of DRSCA (median 44 points), followed by patients with social support-focused coping (median 40 points) and problem-focused coping (median 36 points), while patients with avoidance-focused coping had the lowest SDSCA total score (33 points). The type of dominant coping mechanism has a significant impact on the quality of the DRSCA measures implemented by the patient to manage their diabetes. Patients with emotion-focused and social support-focused coping styles tend to have significantly increased adherence to DRSCA scores, while patients with other dominant coping styles are less interested in managing their disease.