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《Diagnostic and interventional imaging》2020,101(12):821-830
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. 相似文献
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《Diagnostic and interventional imaging》2020,101(9):555-564
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%. 相似文献
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《Journal of pain and symptom management》2020,59(4):794-807.e4
ContextPalliative care is gaining ground globally and is endorsed in high-level policy commitments, but service provision, supporting policies, education, and funding are incommensurate with rapidly growing needs.ObjectivesThe objective of this study was to describe current levels of global palliative care development and report on changes since 2006.MethodsAn online survey of experts in 198 countries generated 2017 data on 10 indicators of palliative care provision, fitted to six categories of development. Factor analysis and discriminant analysis showed the validity of the categorization. Spearman correlation analyses assessed the relationship with World Bank Income Level (WBIL), Human Development Index (HDI), and Universal Health Coverage (UHC).ResultsNumbers (percentages) of countries in each development category were as follows: 1) no known palliative care activity, 47 (24%); 2) capacity-building, 13 (7%); 3a) isolated provision, 65 (33%); 3b) generalized provision, 22 (11%); 4a) preliminary integration into mainstream provision, 21 (11%); 4b) advanced integration, 30 (15%). Development levels were significantly associated with WBIL (rS = 0.4785), UHC (rS = 0.5558), and HDI (rS = 0.5426) with P < 0.001. Net improvement between 2006 and 2017 saw 32 fewer countries in Categories 1/2, 16 more countries in 3a/3b, and 17 more countries in 4a/4b.ConclusionPalliative care at the highest level of provision is available for only 14% of the global population and is concentrated in European countries. An 87% global increase in serious health-related suffering amenable to palliative care interventions is predicted by 2060. With an increasing need, palliative care is not reaching the levels required by at least half of the global population. 相似文献
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目的 探讨思维导图在本科护生精神科实习带教中的应用效果。方法 选取2018年6月至12月31名本科护生为试验组,采用思维导图进行带教;选取2019年4月至7月32名本科护生作为对照组,采用传统的带教方法进行带教。比较两组前后评判性思维能力、对带教方法的评价,以及两组护生的理论、操作考试成绩。采用SPSS 20.0进行t检验、卡方检验和秩和检验。结果 试验组在评判性思维能力量表各项得分明显提高,由带教前的(248.09±24.45)分提高至(331.03±14.31)分,且高于对照组(P<0.05)。试验组在带教方法肯定性评价各项得分均高于对照组,差异均有统计学意义(P<0.05)。试验组带教后理论成绩为(88.35±7.71)分、操作考试成绩为(86.45±5.03)分,对照组带教后理论成绩为(74.41±10.27)分、操作考试成绩为(81.02±6.64)分,两组比较差异有统计学意义(P<0.05)。结论 运用思维导图对精神科护生进行临床带教,提高了护生学习积极性,学习由被动变主动,同时促进了护生评判性思维能力培养,值得临床推广。 相似文献
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目的探讨PBL与思维导图相结合教学模式在临床教学中的应用。方法本文研究对象为在我院见习的临床本科生,为大理大学2015级一个班级,共55名学生,数据收集时间为2019年1月;按照授课方式的差异将学生分为实验班与对照班,实验班27名学生,对照班28名学生;对照班实施常规教学法,实验班实施PBL与思维导图相结合教学法。结果实验班教学结束测试得分为(85.60±3.46)分,对照班教学结束测试得分为(77.48±3.12)分,差异具有统计学意义(P<0.05);实验班教学方式满意度评分为(90.35±4.12)分,对照班教学方式满意度评分为(80.25±4.05)分,差异具有统计学意义(P<0.05)。结论在临床教学中采用PBL与思维导图相结合教学模式,能够提高学生的专业知识掌握水平与对教学模式的认可度。 相似文献
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《Clinical neurophysiology》2020,131(5):1087-1098
ObjectiveFunctional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis.MethodsWe introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1340 visually marked IEDs. Differences in network structure and strength were assessed.ResultsIEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure.ConclusionsIncreases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS.SignificanceDynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy. 相似文献
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Anna‐Mari Hekkala Heikki Vnnen Heikki Swan Lasse Oikarinen Matti Viitasalo Lauri Toivonen 《Annals of noninvasive electrocardiology》2006,11(4):318-326
Background: Accurate measurement of the QT interval is important for diagnosing long QT syndrome (LQTS), and in research on determinants of ventricular repolarization time. We tested automatic analysis of QT intervals from multiple ECG leads on chest. Methods: Eleven healthy volunteers and 10 genotyped LQTS patients were tested at rest and during exercise with a bicycle ergometer twice 1–31 months apart. Electrocardiograms were recorded with the body surface potential mapping system, and 12 precordial channels were selected for analysis. Averaged QT peak and QT end intervals were determined with an automated algorithm, and the difference QT end minus QT peak (Tp‐e) was calculated. Repeatability was assessed by coefficient of variation (CV) between measurements. Results: Within one test at rest the QT end intervals were highly repeatable with CV 0.6%. In repeated tests CV was 4.4% for QT end interval and 3.5% when the QT interval was corrected for heart rate. In exercise test at specified heart rates, mean CV was 3.0% for QT end and 2.9% for QT peak interval. The CV of Tp‐e interval was 10.2% at rest, and 9.3% in exercise test. Reproducibility was comparable between healthy subjects and LQTS patients. Conclusions: The BSPM system with automated analysis produced accurate and highly repeatable QT interval measurements. Reproducibility was adequate also over prolonged time periods both at rest and in exercise stress test. The method can be applied in studying duration of ventricular repolarization time in different physiologic and pharmacologic interventions. 相似文献