Update on adrenal cortical neoplasia

The adrenal cortex gives rise to a biologically heterogenous group of neoplasms, each with a distinct morphology, antigen expression and molecular profile. Adrenal cortical adenomas have excellent prognosis and are usually cured by surgical resection alone, while adrenal cortical carcinomas are very aggressive tumors with a poor prognosis regardless of therapy. These tumors are rare and often challenging for a pathologist to diagnose, as significant overlap exists between benign and malignant lesions in some cases. In this review, we attempt to summarize most important histologic and clinical features of adrenal cortical adenomas and carcinomas, clarify the use of different grading systems, the use of special stains and the differential diagnosis for practicing pathologists. Most relevant hereditary syndromes associated with adrenal cortical tumors are listed. Updates in molecular alterations in adrenal cortical neoplasms and hyperplastic diseases as well as their clinical significance and potential therapeutic implications are also discussed.     

国内外安宁疗护准入标准的研究进展

安宁疗护可提高患者在生命终末阶段的生活质量，减轻患者和家属的身心痛苦。科学合理的安宁疗护准入标准可帮助医护人员识别出需要安宁疗护服务的患者，使其及时获得安宁疗护服务，因此明确安宁疗护的准入标准是推进安宁疗护发展的基础。本文就国内外安宁疗护准入标准的制定方法、具体内容及优缺点进行综述，以期为我国安宁疗护准入标准的构建提供参考。     

Angiopathie amyloïde cérébrale ou atteinte des microvaisseaux cérébraux en rapport avec l’HTA : un challenge clinique

Cerebral amyloid angiopathy (CAA) is a entity characterized by degenerative Amyloïd deposits in the walls of the meningeal and cortical vessels. It is considered as the second cause of primitives cerebral hemorrhage in elderly. The differential diagnosis between AAC and hypertension-related cerebral small vessel diseases is difficult and represent a true challenge for the clinician. We report two cases of cerebral small vessel diseases revealed by malignant hypertension.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

Systematic review: Predictors of students’ success in baccalaureate nursing programs

Nursing schools strive to select a diverse student population who are likely to succeed by ensuring timely student progression through the program and effective use of educational sources. The purpose of this systematic literature review is to explore the preadmission variables and selection criteria that predict student success in 4-year baccalaureate nursing programs in the U.S. Sixteen articles met the eligibility criteria, and six measures were used to define student success: (a) early academic success, particularly during the first and second year; (b) attrition; (c) timely completion of the program; (d) graduation; (e) performance in nursing courses; and (f) academic performance in other science courses. Typically, the core set of cognitive predictors used in the admission process in nursing schools were pre-nursing GPA, pre-nursing collegiate science GPA, and scores on standardized aptitude exams. This review suggests that it is challenging to isolate one single variable as the best predictor of student success; however, using a combination of variables can offer a reliable prediction method. More researchers should consider using a theoretical basis to guide their inquiry on this topic. Additionally, researchers should examine admission variables that are most relevant across programs.     

Transarterial Chemoembolization vs Radioembolization for Neuroendocrine Liver Metastases: A Multi-Institutional Analysis

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