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1.
Kara S. Tanaka MD Veronica R. Andaya BA Steven W. Thorpe MD Kenneth R. Gundle MD James B. Hayden MD Yee-Cheen Duong MD Raffi S. Avedian MD David G. Mohler MD Lee J. Morse MD Melissa N. Zimel MD Richard J. O'Donnell MD Andrew Fang MD Robert Lor Randall MD Tina H. Tran BS Christin New BA Rosanna L. Wustrack MD other members of Study Group FORCE 《Journal of surgical oncology》2023,127(1):148-158
2.
Michael Staehler Peter J. Goebell Lothar Müller Till-Oliver Emde Natalie Wetzel Lisa Kruggel Martina Jänicke Norbert Marschner the RCC-Registry Group 《International journal of cancer. Journal international du cancer》2020,146(5):1307-1315
Non-clear cell renal cell carcinoma is a very rare malignancy that includes several histological subtypes. Each subtype may need to be addressed separately regarding prognosis and treatment; however, no Phase III clinical trial data exist. Thus, treatment recommendations for patients with non-clear cell metastatic RCC (mRCC) remain unclear. We present first prospective data on choice of first- and second-line treatment in routine practice and outcome of patients with papillary mRCC. From the prospective German clinical cohort study (RCC-Registry), 99 patients with papillary mRCC treated with systemic first-line therapy between December 2007 and May 2017 were included. Prospectively enrolled patients who had started first-line treatment until May 15, 2016, were included into the outcome analyses (n = 82). Treatment was similar to therapies used for clear cell mRCC and consisted of tyrosine kinase inhibitors, mechanistic target of rapamycin inhibitors and recently checkpoint inhibitors. Median progression-free survival from start of first-line treatment was 5.4 months (95% confidence interval [CI], 4.1–9.2) and median overall survival was 12.0 months (95% CI, 8.1–20.0). At data cutoff, 73% of the patients died, 6% were still observed, 12% were lost to follow-up, and 9% were alive at the end of the individual 3-year observation period. Despite the lack of prospective Phase III evidence in patients with papillary mRCC, our real-world data reveal effectiveness of systemic clear cell mRCC therapy in papillary mRCC. The prognosis seems to be inferior for papillary compared to clear cell mRCC. Further studies are needed to identify drivers of effectiveness of systemic therapy for papillary mRCC. 相似文献
3.
L. Umapathy G.G. Perez-Carrillo M.B. Keerthivasan J.A. Rosado-Toro M.I. Altbach B. Winegar C. Weinkauf A. Bilgin for the Alzheimers Disease Neuroimaging Initiative 《AJNR. American journal of neuroradiology》2021,42(4):639
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. 相似文献
4.
5.
国家癌症中心/国家肿瘤质控中心 《中华放射肿瘤学杂志》2020,29(11):925-931
放疗记录与验证系统(RVS)是一套用于防止医用电子加速器等放疗设备治疗参数设置错误,并且记录所有放疗阶段执行参数的医用计算机软件控制系统。为确保患者的治疗安全,必须对记录与验证系统采取必要的质量控制措施。本指南内容涉及:RVS安装和参数设定过程中的质量控制;RVS的验收测试;RVS在临床使用过程中的持续质量控制;使用RVS过程中的典型错误类型;执行RVS验收测试的具体测试例。 相似文献
6.
The National DMEFP Conference Team which consists of the following authors Charlene M. Dewey Teri L. Turner Linda Perkowski Jean Bailey Larry D. Gruppen 《Medical teacher》2016,38(2):141-149
Medical education fellowship programs (MEFPs) are a form of faculty development contributing to an organization’s educational mission and participants’ career development. Building an MEFP requires a systematic design, implementation, and evaluation approach which aligns institutional and individual faculty goals. Implementing an MEFP requires a team of committed individuals who provide expertise, guidance, and mentoring. Qualified MEFP directors should utilize instructional methods that promote individual and institutional short and long term growth. Directors must balance the use of traditional design, implementation, and evaluation methodologies with advancing trends that may support or threaten the acceptability and sustainability of the program. Drawing on the expertise of 28 MEFP directors, we provide twelve tips as a guide to those implementing, sustaining, and/or growing a successful MEFP whose value is demonstrated by its impacts on participants, learners, patients, teaching faculty, institutions, the greater medical education community, and the population’s health. 相似文献
7.
Characteristics of hyperparathyroid states in the Canadian multicentre osteoporosis study (CaMos) and relationship to skeletal markers 下载免费PDF全文
8.
9.
K. Bhatia Group of Obstetric Anaesthetists of Lancashire Greater Manchester Mersey Study Collaborators 《Anaesthesia》2022,77(4):389-397
Since the start of the COVID-19 pandemic, few studies have reported anaesthetic outcomes in parturients with SARS-CoV-2 infection. We reviewed the labour analgesic and anaesthetic interventions utilised in symptomatic and asymptomatic parturients who had a confirmed positive test for SARS-CoV-2 across 10 hospitals in the north-west of England between 1 April 2020 and 31 May 2021. Primary outcomes analysed included the analgesic/anaesthetic technique utilised for labour and caesarean birth. Secondary outcomes included a comparison of maternal characteristics, caesarean birth rate, maternal critical care admission rate along with adverse composite neonatal outcomes. A positive SARS-CoV-2 test was recorded in 836 parturients with 263 (31.4%) reported to have symptoms of COVID-19. Neuraxial labour analgesia was utilised in 104 (20.4%) of the 509 parturients who went on to have a vaginal birth. No differences in epidural analgesia rates were observed between symptomatic and asymptomatic parturients (OR 1.03, 95%CI 0.64–1.67; p = 0.90). The neuraxial anaesthesia rate in 310 parturients who underwent caesarean delivery was 94.2% (95%CI 90.6–96.0%). The rates of general anaesthesia were similar in symptomatic and asymptomatic parturients (6% vs. 5.7%; p = 0.52). Symptomatic parturients were more likely to be multiparous (OR 1.64, 95%CI 1.19–2.22; p = 0.002); of Asian ethnicity (OR 1.54, 1.04–2.28; p = 0.03); to deliver prematurely (OR 2.16, 95%CI 1.47–3.19; p = 0.001); have a higher caesarean birth rate (44.5% vs. 33.7%; OR 1.57, 95%CI 1.16–2.12; p = 0.008); and a higher critical care utilisation rate both pre- (8% vs. 0%, p = 0.001) and post-delivery (11% vs. 3.5%; OR 3.43, 95%CI 1.83–6.52; p = 0.001). Eight neonates tested positive for SARS-CoV-2 while no differences in adverse composite neonatal outcomes were observed between those born to symptomatic and asymptomatic mothers (25.8% vs. 23.8%; OR 1.11, 95%CI 0.78–1.57; p = 0.55). In women with COVID-19, non-neuraxial analgesic regimens were commonly utilised for labour while neuraxial anaesthesia was employed for the majority of caesarean births. Symptomatic women with COVID-19 are at increased risk of significant maternal morbidity including preterm birth, caesarean birth and peripartum critical care admission. 相似文献
10.
The group of Professor Ning Jiao and Professor Song Song have made new progress in the field of electrophilic halogenation modification of electron-deficient aromatics 下载免费PDF全文