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%.     

GE宝石能谱CT故障分析

简要介绍了GE宝石能谱CT(Discovery CT750 HD)的技术特点,详细阐述了日常使用中出现的间歇性停机、无法进行Fastcal(快速空气校正)以及扫描中断和gantry(扫描架)自动复位3例故障的维修过程,为医院维修人员维修类似故障提供了参考。最后总结了医疗设备常见故障及主要故障原因,提出了快速排查CT故障的方法,并建议在日常使用中及时保养维护以降低设备故障率。     

妊娠期甲状腺癌多学科综合治疗

妊娠期甲状腺癌的诊治是目前甲状腺疾病诊治的热点和难点问题之一。妊娠期发现甲状腺癌对临床医生和孕妇都是一个极大的挑战，应通过多学科协作，尽快明确诊断，全面、合理的评估孕妇和胎儿的情况，选择最合适的治疗和随诊方式，避免对孕妇和胎儿产生副反应，维持正常孕期，获得最佳的治疗效果。     

Maîtrise des risques liés à la production des fluides de dialyse dans une unité de télédialyse

Objective

The “Centre Hospitalier Francois Dunan” is located on an isolated island and ensures patients care in hemodialysis thanks to telemedicine support. Many research studies have demonstrated the importance of hemodialysis fluids composition to reduce morbidity in patients on chronic hemodialysis. The aim of this study was to identify the risks inherent in the production of dialysis fluids in a particular context, in order to set up an improvement action plan to improve risk control on the production of dialysis fluids.

情景模拟联合案例讨论在急诊护理临床带教中的应用体会

目的探究急诊护理临床带教中采用情景模拟、案例讨论联合教学法效果。方法在本院实习的若干名护生中,选取124名护生分按照教学方法分组,对照组62名实施传统急诊护理带教,观察组62名实施案例讨论、情景模拟联合教学法,对比临床带教效果。结果两组实践与理论成绩相比,观察组成绩更高(P<0.05)。思维、急救及应激能力自我评价相比,观察组的总提高率高于对照组(P<0.05)。实施案例分析结合情景模拟教学模式后,问卷调查中观察组无护生不赞同此模式,非常赞同此教学模式的护生占总人数的90%以上。结论实施案例讨论、情景模拟联合教学模式后,护生急诊护理实践与理论水平均提升,护生我评价较高。     

高渗盐水和甘露醇在动脉瘤性蛛网膜下腔出血患者降低颅内压中的应用

目的比较3%高渗盐水和20%甘露醇治疗重症动脉瘤性蛛网膜下腔出血所致颅内压增高的疗效.方法25例动脉瘤性蛛网膜下腔出血患者出现颅内压增高事件时, 随机交替接受等渗透剂量的160 mL 3%高渗盐水与150 mL 20%甘露醇进行降低颅内压治疗, 连续监测患者颅内压、平均动脉压、脑灌注压及中心静脉压.记录有效降低颅内压持续时间、颅内压最大降幅及其时间, 用药前及用药后1 h、3 h血钠水平及血浆渗透压.结果3%高渗盐水和20%甘露醇均可降低颅内压(均 P < 0.01), 两者的降低颅内压作用持续时间及颅内压降幅差异均无统计学意义(均 P >0.05).患者脑灌注压较用药前均上升(均 P < 0.01), 平均动脉压先上升后下降, 但差异无统计学意义( P >0.05).患者中心静脉压稍有波动, 但差异均无统计学意义(均 P >0.05).20%甘露醇治疗后患者血钠下降, 3%高渗盐水治疗后患者血钠值上升, 变化均有统计学意义(均 P < 0.05).20%甘露醇及3%高渗盐水治疗后患者血浆渗透压均先上升后下降, 变化均有统计学意义(均 P < 0.01). 结论3%高渗盐水可作为治疗动脉瘤性蛛网膜下腔出血所致颅内压增高患者的一线治疗药物.     

TGFβ promotes YAP‐dependent AXL induction in mesenchymal‐type lung cancer cells

The acquisition of chemoresistance remains a major cause of cancer mortality due to the limited accessibility of targeted or immune therapies. However, given that severe alterations of molecular features during epithelial‐to‐mesenchymal transition (EMT) lead to acquired chemoresistance, emerging studies have focused on identifying targetable drivers associated with acquired chemoresistance. Particularly, AXL, a key receptor tyrosine kinase that confers resistance against targets and chemotherapeutics, is highly expressed in mesenchymal cancer cells. However, the underlying mechanism of AXL induction in mesenchymal cancer cells is poorly understood. Our study revealed that the YAP signature, which was highly enriched in mesenchymal‐type lung cancer, was closely correlated to AXL expression in 181 lung cancer cell lines. Moreover, using isogenic lung cancer cell pairs, we also found that doxorubicin treatment induced YAP nuclear translocation in mesenchymal‐type lung cancer cells to induce AXL expression. Additionally, the concurrent activation of TGFβ signaling coordinated YAP‐dependent AXL expression through SMAD4. These data suggest that crosstalk between YAP and the TGFβ/SMAD axis upon treatment with chemotherapeutics might be a promising target to improve chemosensitivity in mesenchymal‐type lung cancer.

Abbreviations

AUC

area under the curve

AXL

AXL receptor tyrosine kinase

BCL2

B‐cell lymphoma 2

CTD2

cancer target discovery and development

CTGF

connective tissue growth factor

DEG

differentially expressed genes

DOXO

doxorubicin

EMT

epithelial–mesenchymal transition

Eto

etoposide

FDA

Food and Drug Administration

ITGB3

integrin beta‐3

MAPK

mitogen‐activated protein kinase

MMP2

matrix metalloproteinase‐2

MMP9

matrix metalloproteinase‐9

mRNA

messenger RNA

NF‐κB

nuclear factor kappa‐light‐chain‐enhancer of activated B cells

SBE

SMAD binding element

SERPINE1

serpin family E member 1

siRNA

small interfering RNA

ssGSEA

single‐sample gene set enrichment analysis

TCGA

The Cancer Genome Atlas

TGFβ

transforming growth factor beta

YAP

Yes‐associated protein

YAP8SA

mutants of inhibitory phosphorylation site at eight serine to Alanine of YAP

ZEB1

zinc finger E‐box binding homeobox 1

ZEB2

zinc finger E‐box‐binding homeobox 2