EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer—2020 Update. Part 1: Screening,Diagnosis, and Local Treatment with Curative Intent

ObjectiveTo present a summary of the 2020 version of the European Association of Urology (EAU)-European Association of Nuclear Medicine (EANM)-European Society for Radiotherapy and Oncology (ESTRO)-European Society of Urogenital Radiology (ESUR)-International Society of Geriatric Oncology (SIOG) guidelines on screening, diagnosis, and local treatment of clinically localised prostate cancer (PCa).Evidence acquisitionThe panel performed a literature review of new data, covering the time frame between 2016 and 2020. The guidelines were updated and a strength rating for each recommendation was added based on a systematic review of the evidence.Evidence synthesisA risk-adapted strategy for identifying men who may develop PCa is advised, generally commencing at 50 yr of age and based on individualised life expectancy. Risk-adapted screening should be offered to men at increased risk from the age of 45 yr and to breast cancer susceptibility gene (BRCA) mutation carriers, who have been confirmed to be at risk of early and aggressive disease (mainly BRAC2), from around 40 yr of age. The use of multiparametric magnetic resonance imaging in order to avoid unnecessary biopsies is recommended. When a biopsy is performed, a combination of targeted and systematic biopsies must be offered. There is currently no place for the routine use of tissue-based biomarkers. Whilst prostate-specific membrane antigen positron emission tomography computed tomography is the most sensitive staging procedure, the lack of outcome benefit remains a major limitation. Active surveillance (AS) should always be discussed with low-risk patients, as well as with selected intermediate-risk patients with favourable International Society of Urological Pathology (ISUP) 2 lesions. Local therapies are addressed, as well as the AS journey and the management of persistent prostate-specific antigen after surgery. A strong recommendation to consider moderate hypofractionation in intermediate-risk patients is provided. Patients with cN1 PCa should be offered a local treatment combined with long-term hormonal treatment.ConclusionsThe evidence in the field of diagnosis, staging, and treatment of localised PCa is evolving rapidly. The 2020 EAU-EANM-ESTRO-ESUR-SIOG guidelines on PCa summarise the most recent findings and advice for their use in clinical practice. These PCa guidelines reflect the multidisciplinary nature of PCa management.Patient summaryUpdated prostate cancer guidelines are presented, addressing screening, diagnosis, and local treatment with curative intent. These guidelines rely on the available scientific evidence, and new insights will need to be considered and included on a regular basis. In some cases, the supporting evidence for new treatment options is not yet strong enough to provide a recommendation, which is why continuous updating is important. Patients must be fully informed of all relevant options and, together with their treating physicians, decide on the most optimal management for them.     

Estimating Local Cellular Density in Glioma Using MR Imaging Data

BACKGROUND AND PURPOSE:Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.MATERIALS AND METHODS:We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.RESULTS:The random forest methodology estimated cellular density with R² = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R² = 0.52). Outputs were also reported as graphic maps.CONCLUSIONS:Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.

Increased cellular density (CD) is a hallmark of cancer and a key feature in histologic glioma analysis.¹ Mapping cellular density throughout a tumor would be a valuable tool to probe how tumors infiltrate and analyze the transition between diseased and healthy brain. However, measuring CD requires tissue, which entails additional risks and is expensive to obtain. There is no currently accepted clinical algorithm to translate imaging data into quantitative assessments of CD.There is great need for a method to estimate CD noninvasively in human patients with gliomas. In this article, we describe the development of such a method using MR imaging data inputs by correlating with multiple biopsy specimens acquired during a prospective human clinical trial. We obtained comprehensive MR imaging, including conventional, diffusion, perfusion, and permeability imaging sequences. We used machine learning approaches to correlate imaging findings with CD measurements from pathology, devised an algorithm to estimate CD from MR imaging inputs, and generated CD maps for the visual display of the predictions. We identified the most informative imaging data subset. This work has multiple applications in the diagnosis and treatment of patients with gliomas: For example, the method can be used to guide biopsy, resection, and surgery and delineate tumor borderzones both pre- and postoperatively.²     

Biomarkers for traumatic brain injury: a short review

Neurosurgical Review - Cellular response to TBI is a mixture of excitotoxicity, neuroinflammation, and cell death. Biomarkers that can track these lesions and inflammatory processes are being...     

Timing of elective surgery and risk assessment after SARS-CoV-2 infection: an update

The impact of vaccination and new SARS-CoV-2 variants on peri-operative outcomes is unclear. We aimed to update previously published consensus recommendations on timing of elective surgery after SARS-CoV-2 infection to assist policymakers, administrative staff, clinicians and patients. The guidance remains that patients should avoid elective surgery within 7 weeks of infection, unless the benefits of doing so exceed the risk of waiting. We recommend individualised multidisciplinary risk assessment for patients requiring elective surgery within 7 weeks of SARS-CoV-2 infection. This should include baseline mortality risk calculation and assessment of risk modifiers (patient factors; SARS-CoV-2 infection; surgical factors). Asymptomatic SARS-CoV-2 infection with previous variants increased peri-operative mortality risk three-fold throughout the 6 weeks after infection, and assumptions that asymptomatic or mildly symptomatic omicron SARS-CoV-2 infection does not add risk are currently unfounded. Patients with persistent symptoms and those with moderate-to-severe COVID-19 may require a longer delay than 7 weeks. Elective surgery should not take place within 10 days of diagnosis of SARS-CoV-2 infection, predominantly because the patient may be infectious, which is a risk to surgical pathways, staff and other patients. We now emphasise that timing of surgery should include the assessment of baseline and increased risk, optimising vaccination and functional status, and shared decision-making. While these recommendations focus on the omicron variant and current evidence, the principles may also be of relevance to future variants. As further data emerge, these recommendations may be revised.     

一种预测子宫内膜病变患者内膜病理类型的临床模型＊

目的 开发并评估一种用于预测子宫内膜病变患者内膜病理类型的临床模型；方法 选取2019年11月至2021年11月因妇科B超发现子宫内膜病变并于山东中医药大学附属医院行宫腔镜下内膜活检的患者，结合其病史与最小绝对收缩和选择算子法（Least absolute shrinkage and selection operator，LASSO）筛选影响内膜病变的独立危险因素，列线图（nomogram）函数建立列线图模型，采用ROC曲线下方的面积大小（Area Under Curve， AUC）、C指数（C-index）、拟合优度（Hosmer-Lemeshow）检验、自举法（bootstrap）评估及验证模型，依据列线图对纳入患者进行风险赋分，绘制ROC曲线获取风险评分的截断值，从而划分高低风险的人群；结果 阴道流血、绝经、无流产史、并发高血压、B超内膜厚度增厚、内膜回声不正常，中医证型为虚实夹杂证是发生子宫内膜癌及癌前病变的独立危险因素，评估模型所得AUC值与C-index均>0.9，Hosmer-Lemeshow检验显示P>0.05，bootstrap内部验证法所得C-index亦高于0.9，均说明模型准确度、区分度及可信度良好，当患者的风险评分总和≥177分时，属于高风险人群；结论 本模型可以预测B超提示子宫内膜病变患者的内膜病理类型为癌前或恶变型的概率并识别高风险人群。     

Effects of Neonatal Administration of Non-Opiate Analogues of Leu-Enkephalin to Heart Tissue Homeostasis of Prepubertal Albino Rats Exposed to Hypoxia

Bulletin of Experimental Biology and Medicine - Hypobaric hypoxia (pO2 65 mm Hg, duration 4 h) induced a significant increase in the number of cardiomyocytes expressing р53, beclin-1,...     

Incorporating respondent-driven sampling into web-based discrete choice experiments: preferences for COVID-19 mitigation measures

Health Services and Outcomes Research Methodology - To slow the spread of COVID-19, most countries implemented stay-at-home orders, social distancing, and other nonpharmaceutical mitigation...