Search

Background The coronavirus disease 2019 (COVID-19) pandemic led to far-reaching restrictions of social and professional life, affecting societies all over the world. To contain the virus, medical schools had to restructure their curriculum by switching to online learning. However, only few medical schools had implemented such novel learning concepts. We aimed to evaluate students' attitudes to online learning to provide a broad scientific basis to guide future development of medical education. Methods Overall, 3286 medical students from 12 different countries participated in this cross-sectional, web-based study investigating various aspects of online learning in medical education. On a 7-point Likert scale, participants rated the online learning situation during the pandemic at their medical schools, technical and social aspects, and the current and future role of online learning in medical education. Results The majority of medical schools managed the rapid switch to online learning (78%) and most students were satisfied with the quantity (67%) and quality (62%) of the courses. Online learning provided greater flexibility (84%) and led to unchanged or even higher attendance of courses (70%). Possible downsides included motivational problems (42%), insufficient possibilities for interaction with fellow students (67%) and thus the risk of social isolation (64%). The vast majority felt comfortable using the software solutions (80%). Most were convinced that medical education lags behind current capabilities regarding online learning (78%) and estimated the proportion of online learning before the pandemic at only 14%. In order to improve the current curriculum, they wish for a more balanced ratio with at least 40% of online teaching compared to on-site teaching. Conclusion This study demonstrates the positive attitude of medical students towards online learning. Furthermore, it reveals a considerable discrepancy between what students demand and what the curriculum of

Cholangiocarcinoma (CCA) is an aggressive malignancy with a 5-year-survival rate of <10%, mainly due to diagnosis in advanced stages and limited therapeutic options in case of progressive disease. Recently, evidence has indicated that alterations in the SWI/SNF-complex (SWI/SNF) may have an important role in the tumorigenesis of CCA. SWI/SNF-related chromatin remodeling has been reported to be crucial for differentiation and tumor suppression, and loss-of-function mutations of SWI/SNF are present in 20% of human malignancies; however, at present, little is known about its relevance in CCA. In the present study, a cohort of 52 patients with the diagnosis of primary CCA was retrospectively collected. All patients underwent surgery with curative intent. Tissue microarray analysis was performed on each tumor for immunohistochemical loss-of-protein analysis of the SWI/SNF core subunits ARID1A, INI-1, BRG1, PBRM-1 and BRM, corresponding to the following CCA subtypes: Extrahepatic CCA (ECCA), small duct or large duct intrahepatic CCA (ICCA). Kaplan-Meier analysis was used to determine survival distribution and survival differences were evaluated by log-rank test. In total, 14 of 52 patients (similar to 35%) exhibited protein-loss of any tested SWI/SNF core subunit. Notably, 17% of patients exhibited a loss of ARID1a; this was the protein loss with the highest frequency. Patients with small and large duct ICCA with protein-loss of any tested SWI/SNF subunit exhibited significantly worse survival compared with the wild-type cohort with proficient protein expression (P=0.013 and P=0.002), whereas no significant survival difference was detected for patients with ECCA. SWI/SNF and its core subunits may be considered promising predictive and therapeutic targets, and require further investigation in patients with CCA.

Objectives The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network’s performance on internal and external data. Such a network could help improve various radiological workflows. Methods All radiographs from the year 2017 (n = 71,274) acquired at our institution were retrieved from the PACS. The 30 largest categories (n = 58,219, 81.7% of all radiographs performed in 2017) were used to develop and validate a neural network (MobileNet v1.0) using transfer learning. Image categories were extracted from DICOM metadata (study and image description) and mapped to the WHO manual of diagnostic imaging. As an independent, external validation set, we used images from other institutions that had been stored in our PACS (n = 5324). Results In the internal validation, the overall accuracy of the model was 90.3% (95%CI: 89.2–91.3%), whereas, for the external validation set, the overall accuracy was 94.0% (95%CI: 93.3–94.6%). Conclusions Using data from one single institution, we were able to classify the most common categories of radiographs with a neural network. The network showed good generalizability on the external validation set and could be used to automatically organize a PACS, preselect radiographs so that they can be routed to more specialized networks for abnormality detection or help with other parts of the radiological workflow (e.g., automated hanging protocols; check if ordered image and performed image are the same). The final AI algorithm is publicly available for evaluation and extension.

Purpose: Anastomotic leakage is a major surgical complication following esophagectomy and gastric pull-up. Specific risk factors such as celiac trunk (TC) stenosis and high calcification score of the aorta have been identified, but no data are available on their relative prognostic values. This retrospective study aimed to compare and evaluate calcification score versus stenosis quantification with regards to prognostic impact on anastomotic leakage. Patients and methods: Preoperative contrast-enhanced computed tomography scans of 164 consecutive patients with primary esophageal cancer were evaluated by two radiologists to apply a calcification score (0-3 scale) assessing the aorta, the celiac axis and the right and left postceliac arteries. Concurrently, the presence and degree of stenosis of TC and superior mesenteric artery were recorded for stenosis quantification. Results: Anastomotic leakage was noted in 14/164 patients and 12/14 showed stenosis of TC (n=11). The presence of TC stenosis was found to have a significant impact on anastomotic healing (p=0.004). The odds ratio for the prediction of anastomotic leakage by the degree of stenosis was 1.04 (95% CI, 1.02-1.07). Ten of 14 patients had aortic calcification scores of 1 or 2, but calcification scores of the aorta, the celiac axis and the right and left postceliac arteries did not correlate with the corresponding TC stenosis values and showed no influence on patient outcome as defined by the occurrence of anastomotic insufficiency (p=0.565, 0.855, 0.518 and 1.000, respectively). Inter-reader reliability of computed tomography analysis and absolute agreement on calcium scoring was mostly over 90%. No significant differences in preoperative comorbidities and patient characteristics were found between those with and without anastomotic leakage. Conclusion: Measurement of TC stenosis in preoperative contrast-enhanced computed tomography scans proved to be more reliable than calcification scores in predicting an

Purpose: Anastomotic leakage is a major surgical complication following esophagectomy and gastric pull-up. Specific risk factors such as celiac trunk (TC) stenosis and high calcification score of the aorta have been identified, but no data are available on their relative prognostic values. This retrospective study aimed to compare and evaluate calcification score versus stenosis quantification with regards to prognostic impact on anastomotic leakage. Patients and methods: Preoperative contrast-enhanced computed tomography scans of 164 consecutive patients with primary esophageal cancer were evaluated by two radiologists to apply a calcification score (0–3 scale) assessing the aorta, the celiac axis and the right and left postceliac arteries. Concurrently, the presence and degree of stenosis of TC and superior mesenteric artery were recorded for stenosis quantification. Results: Anastomotic leakage was noted in 14/164 patients and 12/14 showed stenosis of TC (n=11). The presence of TC stenosis was found to have a significant impact on anastomotic healing (p=0.004). The odds ratio for the prediction of anastomotic leakage by the degree of stenosis was 1.04 (95% CI, 1.02–1.07). Ten of 14 patients had aortic calcification scores of 1 or 2, but calcification scores of the aorta, the celiac axis and the right and left postceliac arteries did not correlate with the corresponding TC stenosis values and showed no influence on patient outcome as defined by the occurrence of anastomotic insufficiency (p=0.565, 0.855, 0.518 and 1.000, respectively). Inter-reader reliability of computed tomography analysis and absolute agreement on calcium scoring was mostly over 90%. No significant differences in preoperative comorbidities and patient characteristics were found between those with and without anastomotic leakage. Conclusion: Measurement of TC stenosis in preoperative contrast-enhanced computed tomography scans proved to be more reliable than calcification scores in predicting an

De-Hua Chang,1,* Sebastian Brinkmann,2,* Lucy Smith,3 Ingrid Becker,4 Wolfgang Schroeder,2 Arnulf H Hoelscher,5 Stefan Haneder,1 David Maintz,1 Judith Eva Spiro6 1Department of Radiology, University Hospital of Cologne, Cologne, Germany; 2Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany; 3Faculty of Medicine, Memorial University of Newfoundland, St John’s, Canada; 4Institute of Medical Statistics and Computational Biology, University of Cologne, Cologne, Germany; 5Agaplesion Markus Hospital, Frankfurt am Main, Germany; 6Department of Radiology, University Hospital, LMU Munich, Munich, Germany *These authors contributed equally to this work Purpose: Anastomotic leakage is a major surgical complication following esophagectomy and gastric pull-up. Specific risk factors such as celiac trunk (TC) stenosis and high calcification score of the aorta have been identified, but no data are available on their relative prognostic values. This retrospective study aimed to compare and evaluate calcification score versus stenosis quantification with regards to prognostic impact on anastomotic leakage.Patients and methods: Preoperative contrast-enhanced computed tomography scans of 164 consecutive patients with primary esophageal cancer were evaluated by two radiologists to apply a calcification score (0–3 scale) assessing the aorta, the celiac axis and the right and left postceliac arteries. Concurrently, the presence and degree of stenosis of TC and superior mesenteric artery were recorded for stenosis quantification.Results: Anastomotic leakage was noted in 14/164 patients and 12/14 showed stenosis of TC (n=11). The presence of TC stenosis was found to have a significant impact on anastomotic healing (p=0.004). The odds ratio for the prediction of anastomotic leakage by the degree of stenosis was 1.04 (95% CI, 1.02–1.07). Ten of 14 patients had aortic calcification scores of 1 or 2, but calcificatio

Background: Percutaneous endoscopic gastrostomy (PEG) placed in the pull through (PT) technique is a common procedure to restore enteral feeding in patients with swallowing disorders. Limitations of this technique are patients with obstruction of the pharynx or esophagus or with an esophageal stent. We report our experience with the direct puncture (DP) PEG device. Methods: We included 154 patients (55 women). One hundred forty patients had cancer. After passing the endoscope into the stomach, 4 gastropexies were performed with a gastropexy device and the PEG was placed with the introducer method. After 1 month, the sutures were removed and a constant gastrocutaneous fistula had been created and the new catheter could be placed safely. Results: The DP PEG was successfully placed in all patients. Overall complication rate was 11% (minor: 6%, major: 5%). The most common event was tube dislocation (40 cases). In 5 cases of dislocation, this resulted in a major complication with injuring the gastric wall and the necessity for surgical treatment. Conclusions: The DP PEG system is safe, and can be used in cases in which a standard PT PEG is not feasible. To avoid dislocation, strict adherence to a post-interventional protocol is highly recommended. (C) 2016 S. Karger AG, Basel

Background/Aims:The effect of laparoscopic antireflux surgery on esophageal motility is incompletely understood, and any indication for this procedure in patients with motility disorder is disputed in literature. We evaluated the influence of laparoscopic Nissen fundoplication on impaired esophageal motility. Methods: In this pathological manometric study, we divided the patients into two groups preoperatively: the hyponlotility group (mean amplitude of esophageal contraction wave <40 mm Hg; HYPO group, n = 11) and the normal group (mean amplitude of esophageal contraction wave >40 mm Hg; NORM group, n = 43). The amplitudes of esophageal contraction waves 3 and 8 cm above the lower esophageal sphincter and the percentage of peri-staltic contraction waves of the tubular esophagus were analyzed pre- and postoperatively. Results: In total, 54 patients with GERD underwent esophageal manometry before and 6 months after Nissen fundoplication. The length and pressure of the lower esophageal sphincter were increased in both groups postoperatively (p <0.01). Patients in the HYPO group (n = 11) showed a statistically significant increase of mean amplitude of esophageal contraction (32.8 vs. 57.3 mm Hg; p <0.01), while no change was found in the NORM group (n = 43). A total of 72% of patients with preoperative motility disorder showed normal postoperative manometry. Conclusion: Nissen fundoplication normalizes esophageal motility, especially in patients with preoperative hypomotility. Patients with impaired esophageal motility should not per se be excluded from antireflux surgery. (C) 2014 S. Karger AG, Basel

Objectives The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network's performance on internal and external data. Such a network could help improve various radiological workflows. Methods All radiographs from the year 2017 (n = 71,274) acquired at our institution were retrieved from the PACS. The 30 largest categories (n = 58,219, 81.7% of all radiographs performed in 2017) were used to develop and validate a neural network (MobileNet v1.0) using transfer learning. Image categories were extracted from DICOM metadata (study and image description) and mapped to the WHO manual of diagnostic imaging. As an independent, external validation set, we used images from other institutions that had been stored in our PACS (n = 5324). Results In the internal validation, the overall accuracy of the model was 90.3% (95%CI: 89.2-91.3%), whereas, for the external validation set, the overall accuracy was 94.0% (95%CI: 93.3-94.6%). Conclusions Using data from one single institution, we were able to classify the most common categories of radiographs with a neural network. The network showed good generalizability on the external validation set and could be used to automatically organize a PACS, preselect radiographs so that they can be routed to more specialized networks for abnormality detection or help with other parts of the radiological workflow (e.g., automated hanging protocols; check if ordered image and performed image are the same). The final AI algorithm is publicly available for evaluation and extension.

Objectives The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network's performance on internal and external data. Such a network could help improve various radiological workflows. Methods All radiographs from the year 2017 (n = 71,274) acquired at our institution were retrieved from the PACS. The 30 largest categories (n = 58,219, 81.7% of all radiographs performed in 2017) were used to develop and validate a neural network (MobileNet v1.0) using transfer learning. Image categories were extracted from DICOM metadata (study and image description) and mapped to the WHO manual of diagnostic imaging. As an independent, external validation set, we used images from other institutions that had been stored in our PACS (n = 5324). Results In the internal validation, the overall accuracy of the model was 90.3% (95%CI: 89.2-91.3%), whereas, for the external validation set, the overall accuracy was 94.0% (95%CI: 93.3-94.6%). Conclusions Using data from one single institution, we were able to classify the most common categories of radiographs with a neural network. The network showed good generalizability on the external validation set and could be used to automatically organize a PACS, preselect radiographs so that they can be routed to more specialized networks for abnormality detection or help with other parts of the radiological workflow (e.g., automated hanging protocols; check if ordered image and performed image are the same). The final AI algorithm is publicly available for evaluation and extension.