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%.     

液质联用技术鉴定清血八味片化学成分研究

目的:采用高效液相色谱-三重四级杆质谱(HPLC-MS/MS)测定清血八味片中的化学成分。方法:在负离子条件下采用Halo C_(18)色谱柱(2.1 mm×100 mm,2.7μm),以0.1%甲酸-10 mmol/L甲酸铵水和乙腈为流动相进行梯度洗脱,质谱采用Scan模式和MRM模式,检测清血八味片中的化学成分。结果:共鉴定出紫草中成分7种、土木香中成分3种、人工牛黄中成分4种、栀子中成分7种、瞿麦中成分7种、甘草中成分12种。结论:该方法快速可靠,操作简便,可用于清血八味片的质量控制研究,为临床药物使用提供一定的依据。     

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。     

不同规格怀牛膝不同极性部位HPLC指纹图谱

目的研究并比较不同规格怀牛膝不同极性部位的HPLC指纹图谱,探索其内部质量差异,为该药材的规格标准完善及临床用药提供参考。方法将怀牛膝用体积分数75%乙醇水浴回流提取,得体积分数75%乙醇回流提取物,用40 mL水溶解后,依次用石油醚、三氯甲烷、乙酸乙酯、正丁醇进行萃取,得到各萃取相及萃取后的水相,浓缩至浸膏,采集各部位HPLC指纹图谱;运用相似度、综合聚类法进行数据分析,同时对其不同部位的指纹图谱进行比对分析。结果石油醚、氯仿部位均标定了11个共有峰,乙酸乙酯部位标定了10个共有峰,正丁醇部位标定了19个共有峰,水部位标定8个共有峰;氯仿部位指纹图谱相似度差异较大,其他部位指纹图谱相似度差异较小,均在0.9以上;石油醚部位指纹图谱差异主要体现在峰高,乙酸乙酯部位、氯仿部位、水部位的化学成分种类及峰高均存在差异;综合聚类分析能将不同规格怀牛膝区分开。结论不同规格怀牛膝内部质量存在差异;实验中所建立的HPLC指纹图谱可以全面反映不同规格怀牛膝的化学成分分布,为不同规格怀牛膝的整体质量评价提供参考。     

不同氢溴酸右美沙芬口服固体制剂的溶出度考察

目的：对6个厂家不同氢溴酸右美沙芬口服固体制剂进行体外溶出度考察，比较体外溶出情况，为临床用药提供参考。方法：采用转篮法，转速100 r·min^-1，用高效液相色谱法测定氢溴酸右美沙芬口服固体制剂在0.1 mol·L^-1盐酸溶液中的溶出曲线；以威布尔方程拟合溶出参数T₅₀、T_d、m，并对参数进行方差分析。结果：氢溴酸右美沙普通片、分散片、胶囊以及软胶囊的平均累积溶出度分别为94.3%、101.3%、105.2%、93.4%。溶出参数T₅₀、T_d差异较大，其中T₅₀最大的是最小的13.4倍。结论：氢溴酸右美沙片、分散片、胶囊以及软胶囊体外溶出行为差别大，产品质量存在较大差异。