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%.     

凉血通瘀方干预高血压大鼠急性脑出血模型对脑组织中差异miRNA表达的影响＊

目的 研究凉血通瘀方对高血压大鼠急性脑出血模型脑组织miRNA表达的影响，对差异表达的miRNA靶基因进行分析，探索凉血通瘀方可能的药效机制。方法 将自发性高血压大鼠随机分成对照组（B）和实验组（C）。适应性饲养一周后，C组灌胃凉血通瘀方，B组灌胃等体积生理盐水，连续5天，每天1次。构建脑出血模型后收集脑组织，借助全转录组测序技术获得miRNA表达量，与miRBase数据库比对获取已知miRNA，使用miRDeep2预测新miRNA。差异分析软件为DESeq2，筛选阈值为|log₂FC| ≥1 并且P <0.05。对显著差异表达的miRNA进行靶基因预测，对靶基因进行GO功能、KEGG通路富集和PPI网络分析。结果 实验组和对照组对比，共发现21个显著差异表达的miRNA，上调有9个，下调有12个，共预测得到1243个有统计学意义的靶基因。GO富集分析发现，生物过程中突触囊泡分泌的调节、神经递质分泌的调节和神经递质运输的调节占前三位，神经元投射终点、全膜、质膜区域和细胞投射则是主要的细胞成分。分子功能分别为小GTPase绑定、底物特异性跨膜转运蛋白活性和离子跨膜转运体活性。通路分析结果显示，靶基因在癌证通路、pI3K-Akt信号通路、人类乳头瘤病毒感染、神经活性配体-受体相互作用和MAPK通路等分布广泛。采用STRING网站和Cytoscape软件，根据MCC算法筛选出ADRA2C、CASR、CCL28、CCR1、DRD2、GNAT3、GRM2、DYNC1LI1、GABBR1、GNAI1等核心靶基因。结论 凉血通瘀方对脑出血急性期鼠脑组织内miRNA的表达有重要影响；显著差异表达miRNAs可能通过靶向核心基因调控凉血通瘀方干预急性脑出血的病理过程及预后。     

PCI和静脉溶栓治疗急性心肌梗死的疗效比较

目的探讨经皮冠状动脉介入(PCI)和静脉溶栓在急性心肌梗死患者中的应用价值。方法选择我院2013年1月至2014年9月收治的60例急性心肌梗死患者作为研究对象,随机分为实验组和对照组各30例。实验组患者采用PCI治疗,对照组患者采用静脉溶栓治疗,比较两组患者血管再通率、死亡发生率、ST段回落情况、住院时间、近远期不良事件发生率、左室舒张末径和左室射血分数变化情况。结果实验组血管再通率显著高于对照组,差异有统计学意义(P<0.05),而近期和远期不良事件发生率均低于对照组,近期不良事件发生率组间比较差异有统计学意义(P<0.05),而远期不良事件发生率比较差异无统计学意义(P>0.05)。实验组患者死亡发生率、ST段回落和住院时间均低于对照组,差异均有统计学意义(P<0.05)。实验组左室射血分数在术后1个月、3个月和6个月时均显著高于对照组,差异均有统计学意义(P<0.05)。结论急性心肌梗死患者应用PCI治疗可显著提高血管再通率、降低死亡和不良事件发生率,有效改善心功能。     

Prediction and Detection of Secondary Hemorrhages after Thrombolytic Therapy in Ischemic Stroke

Abstract It is well established that thrombolytic therapy increases the risk of secondary intracerebral hemorrhage in ischemic stroke patients. However, the term “intracerebral hemorrhage” (ICH) covers a wide spectrum from tiny spots of blood to massive space-occupying hematoma. We will review the etiology and clinical consequences of secondary hemorrhage after thrombolysis in ischemic stroke patients and discuss the ability of magnetic resonance imaging (MRI) to predict this phenomenon. MRI is a highly sensitive tool for detection of hemorrhagic transformation after ischemic stroke. The definitions of a so-called symptomatic hemorrhage after ischemic infarction differ considerably and will also be described. Attributing a causal relationship of a clinical deterioration to a secondary hemorrhage is not easy and should be only addressed when it exceeds at least 30% of the infarct volume. In other patients, secondary hemorrhage might be regarded as side effect of reperfusion within the region with the most severe perfusion deficit. Cerebral microbleeds (CMBs) are a frequent finding in patients with leukoaraiosis and appear to be a general marker of various types of bleeding- prone small vessel disease and a predictor of recurrent vascular events. Current data do not support the hypothesis that the detection of CMBs is a useful diagnostic criterion for the exclusion of patients with CMBs from thrombolytic therapy. However, an increased risk for the rare patients with numerous CMBs can not be ruled out.