Distinguishing lipoma from liposarcoma is challenging on conventional MRI examination. In case of uncertain diagnosis following MRI, further invasive procedure (percutaneous biopsy or surgery) is often required to allow for diagnosis based on histopathological examination. Radiomics and machine learning allow for several types of pathologies encountered on radiological images to be automatically and reliably distinguished. The aim of the study was to assess the contribution of radiomics and machine learning in the differentiation between soft-tissue lipoma and liposarcoma on preoperative MRI and to assess the diagnostic accuracy of a machine-learning model compared to musculoskeletal radiologists. 86 radiomics features were retrospectively extracted from volume-of-interest on T1-weighted spin-echo 1.5 and 3.0 Tesla MRI of 38 soft-tissue tumors (24 lipomas and 14 liposarcomas, based on histopathological diagnosis). These radiomics features were then used to train a machine-learning classifier to distinguish lipoma and liposarcoma. The generalization performance of the machine-learning model was assessed using Monte-Carlo cross-validation and receiver operating characteristic curve analysis (ROC-AUC). Finally, the performance of the machine-learning model was compared to the accuracy of three specialized musculoskeletal radiologists using the McNemar test. Machine-learning classifier accurately distinguished lipoma and liposarcoma, with a ROC-AUC of 0.926. Notably, it performed better than the three specialized musculoskeletal radiologists reviewing the same patients, who achieved ROC-AUC of 0.685, 0.805, and 0.785. Despite being developed on few cases, the trained machine-learning classifier accurately distinguishes lipoma and liposarcoma on preoperative MRI, with better performance than specialized musculoskeletal radiologists.

The purpose of the study was to investigate whether experiencing fear of dying after acute coronary syndrome predicts later posttraumatic stress symptoms. We enrolled 90 patients hospitalized with main diagnosis of acute coronary syndrome and assessed baseline characteristics. One month after discharge, we collected the Posttraumatic Stress Scale. A total of 24 patients (26.7%) developed posttraumatic stress symptoms 1 month after the acute coronary syndrome event. Patients with posttraumatic stress symptoms reported significantly greater fear of dying, helplessness, avoidance-focused coping, and severe anxiety. In our prospective study, fear of dying was associated with occurrence of posttraumatic stress symptoms in patients hospitalized with acute coronary syndrome.

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Objectives Bullying is a problem of the psychosocial work environment and affects health of the employees through the mechanism of stress. We investigated the prevalence of bullying and cardiovascular health effects among teachers of the secondary schools in Kaunas, Lithuania. Methods A random sample of 738 teachers of 7 Kaunas secondary schools were included into the study. 475 (64.4%) answered the Negative Acts Questionnaire, and some questions on perceived stress, outcomes, diagnosed by a physician over the last 6 months, smoking habits, body height and weight. Cardiovascular diseases included arterial hypertension, ischaemic heart disease, angina pectoris, myocardial infarction. SPSS 10.0 for windows was used in the statistical analysis and the logistic regression models for the estimation of ORs of bullying on the dependent variables (cardiovascular effects, stress symptoms). Results The prevalence of regular bullying was 6.4%, occasional bullying-19.1%. Signifi cant correlations were found between on July 10, 2020 by guest. Protected by copyright.