Clinical Effectiveness of Erlova in EGFR-Mutated Non-Small Cell Lung Cancer: An Affordable Price with Clinical Benefit

Background: Thyrosin kinase inhibitors (TKIs) is approved for the first line treatment of non-small cell lung cancer (NSCLC) patients with  epidermal growth factor receptor (EGFR) mutation. This study performed to assess clinical effectiveness and safety of Erlova (generic form of Erlotinib). Methods: Somatic mutations of EGFR gene were studied in tumor tissue by polymerase chain reaction (PCR) and bi-directional sequencing in 513 chemonaive and histologically verified lung adenocarcinoma Iranian patients. Patients  with EGFR mutation received Erlova at 150 mg/day  as first line treatment. Primary endpoint was progression free survival (PFS). Results: About 21% (n=109) cases had EGFR mutation. Most EGFR mutations were  occurred at exon 19. Among them, sixty nine patients treated with Erlova. Median PFS was 11.4 months and objective response rate (ORR) was about  88%. Most frequent treatment related adverse events was  skin rash. Conclusion: Our findings showed Erlova had remarkable effectiveness. In  mutation-positive patients with EGFR, Erlova can be used  safely instead of  other tyrosine-kinase inhibitors.     

hsacirc0006867在结直肠癌组织中的表达及其临床意义北大核心

目的:探讨环状RNA hsa_circ_0006867在结直肠癌中的表达及其与临床病理因素的关系。方法:全转录组测序筛选结直肠癌中特异circRNAs表达谱,挑选出差异表达显著的hsa_circ_0006867,qRT-PCR检测54例结直肠癌组织及癌旁组织中hsa_circ_0006867表达情况,分析其表达水平与结直肠癌临床病理特征的相关性,ROC曲线分析hsa_circ_0006867在结直肠癌中的诊断价值。结果:测序获得circRNAs在结直肠癌中的差异表达谱,qRT-PCR验证hsa_circ_0006867在结直肠癌中表达下调(P<0.05)。其表达水平与肿瘤分化程度和远处转移有关(P<0.05)。ROC曲线显示hsa_circ_0006867诊断结直肠癌AUC为0.851(95%CI:0.775~0.927),当截断值为0.0146时,敏感度为88.46%(95%CI:0.770~0.946),特异度为73.08%(95%CI:0.598~0.832),差异具有统计学意义(P<0.001)。结论:hsa_circ_0006867在结直肠癌中表达下调,与相关临床病理特征密切联系,可作为潜在结直肠癌临床诊断指标。     

中国抗癌协会神经内分泌肿瘤诊治指南（2022年版）

神经内分泌肿瘤（neuroendocrine neoplasm,NEN）是一类起源于肽能神经元和神经内分泌细胞,具有神经内分泌分化并表达神经内分泌标志物的少见肿瘤,可发生于全身各处,以肺及胃肠胰NEN（gastroenteropancreatic neuroendocrine neoplasm, GEP-NEN）最常见。国内外研究数据均提示,NEN的发病率在不断上升。美国流行病学调查结果显示,与其他类型肿瘤相比,NEN的发病率上升趋势更为显著。中国抗癌协会神经内分泌肿瘤专委会在现有循证医学证据基础上,结合已有国内外指南和共识,制订了首版中国抗癌协会神经内分泌肿瘤诊治指南,为临床工作者提供参考。     

Proteomic Profiling of Small Extracellular Vesicles Isolated from the Plasma of Vietnamese Patients with Non-Small Cell Lung Cancer Reveals Some Potential Biomarkers

Background: Considering the poor prognosis of non-small cell lung cancer (NSCLC), the objective of this study was to examine the potential of plasma-derived vesicles as a source of lung cancer-specific proteins. Extracellular vesicle (EV) cargos are specific to the source cells, hence they have the potential of being a source of cancer-specific proteins.  Methods: The proteins differently expressed in cancer were determined and derived from EVs isolated from the plasma of NSCLC patients at the National Lung Hospital. To this end, purification was done using gel filtration chromatography and ultracentrifugation. In addition, nano liquid chromatography mass spectrometry (LC–MS/MS) was used for analyzing. Results: Fifty-seven EV-derived proteins related to NSCLC were highlighted in this research. Some of them have not been addressed before, such as EEF1A1 (elongation factor 1-α1), KPNB1 (Importin subunit beta 1), SRC (proto-oncogene tyrosine-protein kinase) and ACTC1 (actin, alpha cardiac muscle 1). This list was further confirmed through a comparison with ExoCarta and Vesiclepedia. Conclusion: This study is the first work to show the involvement of several novel proteins of small EV (EEF1A1, KPNB1, SRC, and ACTC1) in the progression of NSCLC. The results suggested that they could serve as novel biomarkers for non-small cell lung cancer in the future.     

国内三家医院33 例临床隐球菌病病原菌分离、药敏试验和临床分析

目的 了解国内三家医院临床确诊隐球菌病的分布、药敏试验、临床特点以及抗生素治疗等，提高临床对隐球菌病临床特征的认识。方法 回顾性分析2018 ～ 2019 年国内三家医疗机构33 例确诊隐球菌病患者的临床表现、治疗、预后及实验室检查和药敏结果。结果 33 例隐球菌病患者（男性25 例，女性8 例）平均年龄50.4±12.6 岁，10 例无基础疾病。33 株隐球菌中新生隐球菌31 株，格特隐球菌2 株，其中16 株(48.5%) 来自脑脊液，7 株(21.2%) 来自肺穿组织。常见的中枢神经系统的临床症状有发热18 例，头痛14 例，恶心呕吐9 例。药敏结果显示，所有菌株对两性霉素B、氟康唑、伊曲康唑和伏立康唑均没有获得耐药；1 株对氟胞嘧啶有获得性耐药。脑隐球菌病主要选择两性霉素B 进行治疗，肺隐球菌病选择氟康唑，好转15 例，死亡3 例，转院8 例，另有7 例未经治疗转院。结论 隐球菌对人类致病的主要是新生隐球菌和格特隐球菌。中枢神经系统症状以发热、头痛和恶心呕吐最常见，单纯肺隐球菌病无典型的中枢神经系统的症状；绝大多数菌株均没有获得耐药；临床应重视病原学检测，及时使用抗真菌治疗，对疾病的诊治和预后十分重要。     

Prevalence of HER2 Expression and Its Correlation with Clinicopathological Parameters in Gastric or Gastroesophageal Junction Adenocarcinoma in North-East Indian Population

Objective: Human epidermal growth factor receptor 2 (erbb2/HER2) overexpression, has now been implicatedin advanced gastric and gastroesophageal junction cancers. The study was conducted to determine the rate of HER2positivity in patients with locally advanced or metastatic gastric and gastroesophageal adenocarcinoma in North-EastIndia and to assess the impact of various demographic and clinical parameters on HER2 positivity. Methods: A total of68 patients of age >18 years of gastric and gastroesophageal adenocarcinoma diagnosed on histopathological examinationfrom September 2016 to February 2018 at Dr B Borooah Cancer Institute, Assam were enrolled for the observational(epidemiological) study. All patients were subjected to the HER2 immunohistochemistry test using a FDA-approved,standardized test kit. HER2 expression was correlated with various demographic and clinicopathological parameters.Results: The overall rate of HER2 positivity in the population studied was 56% (n=38). The rate was non-significantlyhigher in male, older age group (>60 years) and Hindu population. Similarly, HER2 positivity rate was higher in patientswith well differentiated histology and was more common in patients with stage II and III diseases, but neither of theassociations is statistically significant. HER2 positivity rate was significantly higher in proximal and in GEJ tumours(56% versus 44%, P=0.002). Conclusion: HER2 overexpression was evident in 56% of the North-East Indian patientswith locally advanced and metastatic gastric and gastroesophageal adenocarcinoma. The overexpression correlatedsignificantly with primary tumour site. Routine testing of gastric and gastroesophageal tumours for HER2 expressionis recommended to provide a therapeutic advantage in Indian patients.     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

2010年-2018年甲状腺结节相关SCI论文知识图谱及数据可视化分析

目的:分析2010年至2018年甲状腺结节相关SCI论文，并建模进行定性与定量分析。方法:利用文献计量学中的引文分析、共被引分析、数据可视化、聚类分析等方法，使用CiteSpace分析工具，分析来源于Web of ScienceTM核心合集数据库中有关论文的出版情况、国家、机构、作者、知识基础与研究热点。结果:到2018年10月31日截止，研究了4 618篇论文。发表论文数:美国在国家/地区中排名第一，延世大学在研究机构中排名第一，Kwak JY在所有作者中排名第一。从2010年开始，每年发表的论文数量都在稳步增长，有相当多的论文发表在如《新英格兰杂志》、《THYROID》、《JAMA》、《CELL》等高影响因子的期刊上。甲状腺结节的SCI论文参考文献聚类为6类，分别是papillary-like nuclear feature、current statu、radiofrequency ablation、ultrasound elastography、goiter area、data system。代表研究前沿的关键词是meta-analysis、recommendation、thyroid carcinoma、shear wave elastography与bethesda system。结论:本文通过对国际上甲状腺结节的研究基础、研究热点的分析，阐述了甲状腺结节的研究趋势，为我国医学工作者的研究提供参考。