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61.
62.
《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
63.
《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
64.
文题释义:股骨头坏死中日友好医院分型的有限元分析:根据李子荣等提出的中日友好医院分型,建立股骨头坏死三维模型,分为 M型(内侧型)、C型(中央型)和 L型(外侧型),其中 L型包括L1型(次外侧型)、L2型(极外侧型)和 L3型(全头型)。通过对建立的模型进行有限元分析,为该分型的保髋治疗提供了一定力学依据,显示外侧柱的存留是精准预防塌陷的重要因素,为进一步实现个体化治疗提供力学基础。
腓骨支撑坏死股骨头保髋手术:是对于早中期股骨头坏死需要保留股骨头患者进行的一种手术方式。首先需对股骨头进行髓芯减压,清除一定坏死骨,空腔填塞松质骨(髂骨为主),打压结实后植入腓骨(异体或自体)支撑,给坏死区的提供力学支撑及生物学修复,预防股骨头进一步坏死及塌陷。
背景:研究报道股骨头坏死的保髋疗效与外侧柱存留密切相关,中日友好医院分型是根据三柱结构确立的,对股骨头塌陷的预测准确性高。
目的:建立股骨头坏死中日友好医院分型各分型仿真的三维有限元模型,通过有限元分析各分型腓骨植入的力学变化,探讨外侧柱存留对保髋疗效的意义,为该分型的塌陷精准预测提供基础。
方法:建立正常股骨头、中日友好医院分型(M型、C型、L1型、L2型、L3型)股骨头坏死及其腓骨植入3组11种三维有限元模型,运用ANSYS软件进行有限元分析计算,观察各组模型的最大应力值、最大位移值及股骨头内部载荷传递模式。
结果与结论:①坏死组位移最大,应变最大,且因坏死分型不同而位移不同,位移变化如下:M型相似文献
65.
66.
《Biomaterials》2015
Controlling cellular alignment is critical in engineering intestines with desired structure and function. Although previous studies have examined the directional alignment of cells on the surface (x–y plane) of parallel fibers, quantitative analysis of the cellular alignment inside implanted scaffolds with oriented fibers has not been reported. This study examined the cellular alignment in the x–z and y–z planes of scaffolds made with two layers of orthogonally oriented fibers. The cellular orientation inside implanted scaffolds was evaluated with immunofluorescence. Quantitative analysis of coherency between cell orientation and fiber direction confirmed that cells aligned along the fibers not only on the surface (x–y plane) but also inside the scaffolds (x–z & y–z planes). Our study demonstrated that two layers of orthogonally aligned scaffolds can generate the histological organization of cells similar to that of intestinal circular and longitudinal smooth muscle. 相似文献
67.
Primary cutaneous anaplastic large cell lymphomas with 6p25.3 rearrangement exhibit particular histological features
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68.
Rebecca L. King Albert C. Yan Debora R. Sekiguchi John K. Choi 《Journal of cutaneous pathology》2015,42(12):1012-1017
Reactive lymphoid infiltrates of the skin composed predominantly of gamma‐delta (γδ) T cells are not well described in the literature. Herein we report a case of an otherwise healthy 4‐year‐old male who presented with a waxing and waning papular rash characterized by small, discrete crusted papules spread across his trunk, face and extremities. Clinical evaluation revealed no evidence of systemic disease. Microscopic examination revealed a dermal, perivascular infiltrate of highly atypical lymphocytes with a γδ T cell phenotype, worrisome for primary cutaneous γδ T cell lymphoma. The clinical course, however, was that of a reactive condition and prompted consideration of a diagnosis of pityriasis lichenoides et varioliformis acuta (PLEVA) and lymphomatoid papulosis (LyP). In many ways, this case defies current classification schemes and seems to expand the spectrum of reactive γδ T cell infiltrates of the skin. 相似文献
69.
背景与目的:CD44分子是众多肿瘤细胞的标志分子,其表达水平与肿瘤细胞的恶性程度有关。该研究探讨CD44基因中的单核苷酸多态性(single nucleotide polymorphism,SNP)位点与云南汉族人群宫颈癌和非小细胞肺癌(nonsmall cell lung cancer,NSCLC)易感性的相关性。方法:选取了CD44基因中的两个SNP位点rs13347和rs8193,采用TagMan基因分型的方法,分析这两个多态性位点在497例宫颈癌患者和500例健康对照个体以及483例NSCLC患者和471例健康对照个体中的分布特征,并分析CD44基因中的多态性位点与云南汉族人群宫颈癌和NSCLC的相关性。结果:rs13347和rs8193位点等位基因和基因型在宫颈癌组和对照组中的分布频率的差异无统计学意义(P>0.05)。而在NSCLC组和对照组的比较中:rs13347和rs8193位点等位基因在NSCLC组和对照组中的分布频率的差异有统计学意义(P=0.020和P=0.004);这两个位点基因型在NSCLC组和对照组中分布频率的差异有统计学意义(P=0.027和P=0.020);其中rs13347位点等位基因C在NSCLC组中的分布频率显著高于对照组,可能是NSCLC发生的风险因素(OR=1.250,95% CI:1.035~1.509),rs8193位点等位基因C在对照组中的分布频率显著高于NSCLC组,可能是NSCLC发生的保护性因素(OR=0.768,95%CI:0.641~0.921)。单倍型分析结果显示,rs13347C-rs8193T和rs13347T-rs8193C在NSCLC组和对照组中的分布频率差异有统计学意义(P=0.003和0.022);该结果说明单倍型rs13347C-rs8193T可能是云南汉族人群NSCLC发生的风险性因素(OR=1.316,95%CI:1.096~1.579)。结论:CD44基因中的两个SNP位点rs13347和rs8193可能与云南汉族人群宫颈癌发病风险无关,而可能与云南汉族人群NSCLC具有相关性。 相似文献
70.
Rafiye Ciftciler Haluk Demiroglu Yahya Buyukasık Elifcan Aladag Salih Aksu Ibrahim C. Haznedaroglu Nilgun Sayınalp Osman Ozcebe Umit Yavuz Malkan Hakan Goker 《Clinical Lymphoma, Myeloma & Leukemia》2019,19(3):177-182