Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

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%.     

薄层色谱法在当前中药质量标准中的应用探讨

薄层色谱鉴别在历版《中国药典》中的应用经历了从无到有、从少到多的过程；而薄层扫描含量测定在最近几版《中国药典》中的应用比例逐渐降低。随着对中药质量标准体系要求的进一步提高，薄层色谱法的不足之处陆续显现，如仪器普及率低、设备并不简单、结果重复性和稳定性较差、鉴别速度及准确性不及高效液相色谱法、展开剂毒性大等，逐渐不合时宜。在制定中药质量标准时，研究者不应该墨守成规，薄层色谱鉴别也不应该是雷打不动的定性鉴别必备选项。高效液相色谱法具备完全取代薄层色谱法的可行性，薄层色谱法可作为高效液相色谱法的补充。为充分降低检测成本、缩短检测周期、提高鉴别效率，笔者建议中药质量标准体系应该大幅减少薄层色谱鉴别方法的应用，增加高效液相特征图谱鉴定，尽量做到“一个条件，一张图谱”；除非确有必要，《中国药典》等国家质量标准体系应将薄层色谱鉴别作为推荐方法，而非强制标准。     

浙江省2007－2017年罕见病住院病例特征分析

目的 分析2007-2017年浙江省24 388例罕见病住院病例特征，为制定罕见病防控策略提供依据。方法 收集2007-2017年浙江省10家三级甲等（三甲）医院罕见病住院病例资料和各年度住院数，进行描述性统计分析。结果 罕见病病例共24 388例，占住院总例数的2.69‰（24 388/9 054 201），病例数居前3位的疾病类型依次为"血液和造血器官疾病以及某些涉及免疫机能的异常"（占32.81%，8 001/24 388）、"先天性畸形、变形和染色体异常"（占24.87%，6 065/24 388）和"神经系统疾病"（占19.01%，4 635/24 388）；2007-2017年罕见病病例数呈逐年增长趋势，年均增幅19.69%，而罕见病例数占同期住院总例数比例仅在2016-2017年明显上升，各类型罕见病时间分布呈不同特征；罕见病的病例数男女性别比为1.35：1（13 990/10 398），男女性别比最高的3类疾病依次为"消化系统疾病"（4.45：1，1 180/265）、"损伤、中毒和外因的某些其他后果"（3.51：1，281/80）和"神经系统疾病"（2.26：1，3 213/1 422）；各年龄段罕见病类型、各类型罕见病年龄分布均呈不同特征；病例数居前10位的疾病占全部罕见病例数的53.55%（13 060/24 388），其中前3位疾病分别为成人粒细胞缺乏症（14.41%，3 515/24 388）、皮质基底核退化症（7.60%，1 854/24 388）和亨诺克-舍恩莱因紫癜（6.01%，1 466/24 388）。结论 本研究分析的浙江省2007-2017年24 388例罕见病住院病例的特征资料，是推动我国罕见病的研究、监测或登记数据库构建、制定防控策略的参考依据。     

张立平教授治疗消化道术后胃肠功能紊乱经验

消化道术后胃肠功能紊乱影响着患者们的生活质量。张立平教授认为术后胃肠功能紊乱，证属本虚标实，临床治疗当从调理中焦-脾胃肝为切入点，强调"脾胃为本""中焦一体"的治疗思路，总结出脾胃虚弱、肝郁脾虚两个证型，临证时以培土达木为基本治疗原则，辨证论治，临床疗效显著。     

快速康复护理、中医特色护理对卵巢癌的效果

目的分析快速康复护理联合中医特色护理在卵巢癌围手术期护理中应用效果。方法选择本院100例2018年7月-2019年2月卵巢癌患者。随机分组,快速康复护理组采取快速康复护理,快速康复护理联合中医特色护理组则采取快速康复护理联合中医针灸推拿护理。比较两组护理满意度;卵巢癌疼痛以及不良反应好转的时间;治疗前后患者中医证候积分、QOL量表评分值。结果快速康复护理联合中医特色护理组护理满意度、卵巢癌疼痛以及不良反应好转的时间、中医证候积分、QOL量表评分值相比较快速康复护理组更好,P <0.05。结论快速康复护理联合中医针灸推拿护理卵巢癌效果确切,可改善并发症和缓解疼痛,改善生存质量。     

华细辛和北细辛HPLC特征图谱识别与抗炎靶点及抗炎成分分析

建立华细辛和北细辛HPLC特征图谱并结合聚类分析研究2种来源细辛的识别方法;应用网络药理学方法预测细辛潜在抗炎靶点并寻找潜在抗炎成分。对89批细辛药材(12批华细辛和77批北细辛)的数据进行分析确定了11个特征峰,用对照品、紫外光谱和LC-MS指认了11个特征成分。特征峰面积聚类分析显示华细辛和北细辛被分为2类,且利用特征峰面积比值可实现两者区分,当特征峰9(细辛素)/参照峰S(卡枯醇)峰面积比值大于5时为华细辛,小于2时为北细辛。对119种细辛成分进行网络药理学分析的结果表明细辛抗炎作用可能与COX-2,COX-1,iNOS,MAPK14,LAT4H,NR3C1,PPARG和TNF等8个靶点相关,其中COX-2最为关键,与5种特征成分细辛脂素、芝麻脂素、细辛素、甲基丁香酚和黄樟醚均存在相互作用。此外,细辛脂素、芝麻脂素与iNOS,MAPK14也存在相互作用关系,黄樟醚和细辛素可作用于iNOS,COX-1,LAT4H,甲基丁香酚可作用于COX-1,LAT4H。细辛脂素与芝麻脂素均可作用在COX-2,iNOS和MAPK143个靶点上,提示它们是细辛发挥抗炎作用的活性成分;COX-2分子对接结果和COX-2活性实验结果验证了细辛脂素、芝麻脂素可抑制COX-2活性,为细辛抗炎作用活性成分。基于HPLC特征图谱的华细辛和北细辛识别方法简便易行;预测到的细辛抗炎靶点和抗炎成分为完善细辛质量评价体系奠定了基础。