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。     

Rare patients in routine care: Treatment and outcome in advanced papillary renal cell carcinoma in the prospective German clinical RCC-Registry

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.     

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.     

放射治疗记录与验证系统质量控制指南

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。     

Twelve tips for developing,implementing, and sustaining medical education fellowship programs: Building on new trends and solid foundations

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.