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。     

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.     

Pathophysiology and management of sensitive skin: position paper from the special interest group on sensitive skin of the International Forum for the Study of Itch (IFSI)

The special interest group on sensitive skin of the International Forum for the Study of Itch previously defined sensitive skin as a syndrome defined by the occurrence of unpleasant sensations (stinging, burning, pain, pruritus and tingling sensations) in response to stimuli that normally should not provoke such sensations. This additional paper focuses on the pathophysiology and the management of sensitive skin. Sensitive skin is not an immunological disorder but is related to alterations of the skin nervous system. Skin barrier abnormalities are frequently associated, but there is no cause and direct relationship. Further studies are needed to better understand the pathophysiology of sensitive skin – as well as the inducing factors. Avoidance of possible triggering factors and the use of well-tolerated cosmetics, especially those containing inhibitors of unpleasant sensations, might be suggested for patients with sensitive skin. The role of psychosocial factors, such as stress or negative expectations, might be relevant for subgroups of patients. To date, there is no clinical trial supporting the use of topical or systemic drugs in sensitive skin. The published data are not sufficient to reach a consensus on sensitive skin management. In general, patients with sensitive skin require a personalized approach, taking into account various biomedical, neural and psychosocial factors affecting sensitive skin.     

Quality of decision aids developed for women at average risk of breast cancer eligible for mammographic screening: Systematic review and assessment according to the International Patient Decision Aid Standards instrument

Mammographic screening contributes to a reduction in specific mortality, but it has disadvantages. Decision aids are tools designed to support people's decisions. Because these aids influence patient choice, their quality is crucial. The objective of the current study was to conduct a systematic review of decision aids developed for women eligible for mammographic screening who have an average breast cancer risk and to assess the quality of these aids. The systematic review included articles published between January 1, 1997, and August 1, 2019, in the PubMed, Embase, Cochrane, and PsycInfo databases. The studies were reviewed independently by 2 reviewers. Any study containing a decision aid for women eligible for mammographic screening with an average breast cancer risk was included. Two double-blind reviewers assessed the quality of the selected decision aids using the International Patient Decision Aid Standards instrument, version 3 (IPDASi). Twenty-three decision aids were extracted. Classification of decision aid quality using the IPDASi demonstrated large variations among the decision aids (maximum IPDASi score, 188; mean ± SD score, 132.6 ± 23.8; range, 85-172). Three decision aids had high overall scores. The 3 best-rated dimensions were disclosure (maximum score, 8; mean score, 6.8), focusing on transparency; information (maximum score, 32; mean score, 26.1), focusing on the provision of sufficient details; and probabilities (maximum score, 32; mean score 25), focusing on the presentation of probabilities. The 3 lowest-rated dimensions were decision support technology evaluation (maximum score, 8; mean score, 4.3), focusing on the effectiveness of the decision aid; development (maximum score, 24; mean score, 12.6), evaluating the development process; and plain language (maximum score, 4; mean score, 1.9), assessing appropriateness for patients with low literacy. The results of this review identified 3 high-quality decision aids for breast cancer screening.     

Family history of cancer and subsequent risk of cancer: A large-scale population-based prospective study in Japan

Family history (FH) of cancer is an important factor of increased risk of several cancers. Although the association between FH of cancer and concordant cancer risk has been reported in many previous epidemiological studies, no comprehensive prospective study with adjustment for lifestyle habits has evaluated the association of FH of cancer and concordant cancer risk. We investigated the association between FH of cancer and concordant cancer risk in a Japanese population-based prospective study, initiated in 1990 for cohort I and in 1993 for cohort II. We analyzed data on 103,707 eligible subjects without a history of cancer who responded to a self-administered questionnaire including FH of cancer at baseline. Study subjects were followed through 2012 and analyzed using multivariable-adjusted Cox proportional hazards regression models. During 1,802,581 person-years of follow-up, a total of 16,336 newly diagnosed cancers were identified. Any site (Hazard ratios = 1.11 (95% confidence interval = 1.07–1.15]), esophagus (2.11 [1.00–4.45]), stomach (1.36 [1.19–1.55]), liver (1.69 [1.10–2.61]), pancreas (2.63 [1.45–4.79]), lung (1.51 [1.14–2.00]), uterus (1.93 [1.06–3.51]) and bladder cancers (6.06 [2.49–14.74]) with FH of the concordant cancer were associated with an increased risk compared to those without FH. Our findings suggest that having FH of cancer is associated with an increased risk of several concordant cancer incidences in an Asian population. Enquiring about FH of several types of cancer may be important in identifying groups at high-risk of those cancers.