Pearson McCollum (ocelotdoor37)

795-0.955) and 0.96 (0.914-0.996) for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. MP-CDN accurately classified FLLs detected on four-phase CT as HCC, metastases, benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs. MP-CDN accurately classified FLLs detected on four-phase CT as HCC, metastases, benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs. The risk of gastric cancer increases in patients with -associated chronic atrophic gastritis (CAG). X-ray examination can evaluate the condition of the stomach, and it can be used for gastric cancer mass screening. However, skilled doctors for interpretation of X-ray examination are decreasing due to the diverse of inspections. To evaluate the effectiveness of stomach regions that are automatically estimated by a deep learning-based model for CAG detection. We used 815 gastric X-ray images (GXIs) obtained from 815 subjects. The ground truth of this study was the diagnostic results in X-ray and endoscopic examinations. For a part of GXIs for training, the stomach regions are manually annotated. A model for automatic estimation of the stomach regions is trained with the GXIs. For the rest of them, the stomach regions are automatically estimated. Finally, a model for automatic CAG detection is trained with all GXIs for training. In the case that the stomach regions were manually annotated for only 10 GXIs and 30 GXIs, the harmonic mean of sensitivity and specificity of CAG detection were 0.955 ± 0.002 and 0.963 ± 0.004, respectively. By estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG. By estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG. Pancreatic neuroendocrine neoplasms (pNENs) that produce hormones leading to symptoms are classified as functional tumors, while others are classified as nonfunctional tumors. The traditional view is that functionality is a factor that affects the prognosis of pNEN patients. However, as the sample sizes of studies have increased, researches in recent years have proposed new viewpoints. To assess whether functionality is an independent factor for predicting the prognosis of pNEN patients. From January 2004 to December 2016, data of patients who underwent surgery at the primary site for the treatment of pNENs from the Surveillance, Epidemiology, and End Results (SEER) database and West China Hospital database were retrospectively analyzed. Contemporaneous data from the two databases were analyzed separately as two cohorts and then merged as the third cohort to create a large sample that was suitable for multivariate analysis. From the SEER database, age ( = 0.006) and T stage ( < 0.001) were independent risk factors affecting the survival. From the West China Hospital database, independent prognostic factors were age ( = 0.034), sex ( = 0.032), and grade ( = 0.039). The result of the cohort consisting of the combined populations from the two databases showed that race ( = 0.015), age ( = 0.002), sex ( = 0.032) and T stage ( < 0.001) were independent prognostic factors. In the West China Hospital database and in the total population, nonfunctional pNETs and other functional pNETs tended to have poorer prognoses than insulinoma. However, functionality was not associated with the survival time of patients with pNETs in the multivariate analysis. Functionality is not associated with prognosis. Race, age, sex, and T stage are independent factors for predicting the surviv