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.     

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。     

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.     

Gynecologic Malignancy-Associated Venous Thromboembolism and Predictive Tool at Thammasat University Hospital

Objectives: Aims were to investigate the prevalence and risk factors of venous thromboembolism (VTE) in gynecologic malignancy cases. Value of screening tool (Caprini) for prediction of VTE was also assessed. Study design: A retrospective study of gynecologic malignancy subjects who underwent major gynecological operation via exploratory laparotomy at Thammasat University Hospital, Pathum Thani, Thailand from January 2015 to December 2020. Participants were categorized into VTE and non-VTE groups. Caprini score, associated laboratory and clinical factors of both groups were evaluated. Results: A total of 392 subjects were recruited into the study. Prevalence of VTE was 7.4 (29/392) percent. VTE was diagnosed in subjects with endometrial, ovarian and cervical cancer at percentage of 7.8 (15/192), 7.9 (11/138) and 5.7 (3/53), respectively. Demographic characters of both groups were comparable. VTE group had significant more Caprini score, platelets count and platelet lymphocyte ratio (PLR) than non-VTE group. Modified Caprini score (2 multiply Caprini score plus 1 multiply PLR) was generated for better VTE prediction. Sensitivity and specificity of Caprini (≥5.5) and modified Caprini scores (≥22.8) were 72.4 vs 39.4, and 79.3 vs 52.1 percent, respectively. Conclusion: Prevalence of VTE among gynecologic malignancy cases was 7.4 percent. The modified Caprini score was an alternative VTE predictive tool. Cut-off point of modified Caprini score at equal or more than 22.8 was proposed.     

肿瘤的数字治疗北大核心

肿瘤治疗已经进入数字治疗时代。数字治疗是数字健康的重要部分,它是应用数字技术预防、控制和治疗疾病。人工智能分析由生物传感器发来的数据资料,远距离控制肿瘤患者进行虚拟治疗和数字临床试验,模拟现实在线治疗帮助肿瘤患者采取健康行为,这些技术正在改变现有的医学模式。     

转录调节因子c-Myb通过炎症因子Ccl2抑制乳腺癌肺转移的实验研究

目的:研究乳腺癌细胞中转录调节因子c-Myb的表达对肺转移的影响及其分子机制。方法:构建c-Myb高表达的4T1乳腺癌细胞株，种植小鼠构建乳腺癌动物模型，检测实验动物肺转移情况，通过荷瘤抑制试验检测肺转移抑制效果。提取组织进行荧光PCR检测相关炎症因子的表达。利用Medisapiens数据库的生物信息学资源，对c-Myb和Ccl2的表达与乳腺癌患者的预后进行分析。结果:高表达c-Myb的荷瘤小鼠肺部转移灶明显减少，其中炎症相关因子表达受限。由数据库分析得出，c-Myb高表达患者生存期得以延长。结论:乳腺癌中c-Myb的表达能够通过炎症因子Ccl2抑制肺转移从而延长患者生存期。