预测年轻乳腺癌患者生存率列线图的建立和验证

目的:构建预测年轻乳腺癌患者生存情况的列线图，以期帮助临床诊疗。方法:收集SEER数据库中5 525例年轻乳腺癌患者的临床信息，通过单因素Log-rank检验和多因素Cox生存分析筛选出独立预后因素,用于构建预测患者3、5年总生存率(overall survival,OS)和癌症特异性生存率（cancer special survival,CSS)的列线图，将我院就诊的147例年轻乳腺癌患者作为验证集进行外部验证。结果:单因素和多因素分析结果显示，种族、病理类型、组织学分级、T分期、N分期、M分期、ER状态、HER-2状态、手术方式是与患者OS和CSS相关的独立危险因素，将这些因素纳入并建立预测患者OS和CSS的列线图模型。内部和外部验证结果显示模型具有良好的预测性能。基于建立的OS和CSS列线图模型对患者进行了风险分层，能够准确地将年轻乳腺癌患者分成预后有显著差异的三个风险亚组。结论:本研究构建的预测模型能较为准确的预测年轻乳腺癌患者的预后情况，为临床的诊疗提供科学依据。     

Use and Applicability of the Gail Model to Calculate Breast Cancer Risk: A Scoping Review

Objective: To perform a scoping review of the applicability of the Gail model in different countries for different ethnicities. Methods: The review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist and search strategies based on the PICOS approach. The reviewed articles were included if they were published between 2013 and 2018 in English, Portuguese, or Spanish; were original articles available in full online; and described the use of the Gail model. The PubMed, Embase, and Web of Science data bases were searched. Results: A total of 38 articles eligible for analysis were identified, of which 16 used the Gail model to assess breast cancer risk in women, eight analyzed the applicability of this tool in their population, seven compared the tool and/or modified it according to the specific risk factors of their population, and seven cited the model in determining eligibility for chemoprevention. Conclusion: The Gail model has different applicabilities Greater effectiveness and breast cancer risk are found in developed countries.     

Patterns of Medical Care Cost by Service Type for Patients With Recurrent and De Novo Advanced Cancer

ObjectivesThere is limited knowledge about the cost patterns of patients who receive a diagnosis of de novo and recurrent advanced cancers in the United States.MethodsData on patients who received a diagnosis of de novo stage IV or recurrent breast, colorectal, or lung cancer between 2000 and 2012 from 3 integrated health systems were used to estimate average annual costs for total, ambulatory, inpatient, medication, and other services during (1) 12 months preceding de novo or recurrent diagnosis (preindex) and (2) diagnosis month through 11 months after (postindex), from the payer perspective. Generalized linear regression models estimated costs adjusting for patient and clinical factors.ResultsPatients who developed a recurrence <1 year after their initial cancer diagnosis had significantly higher total costs in the preindex period than those with recurrence ≥1 year after initial diagnosis and those with de novo stage IV disease across all cancers (all P < .05). Patients with de novo stage IV breast and colorectal cancer had significantly higher total costs in the postindex period than patients with cancer recurrent in <1 year and ≥1 year (all P < .05), respectively. Patients in de novo stage IV and those with recurrence in ≥1 year experienced significantly higher postindex costs than the preindex period (all P < .001).ConclusionsOur findings reveal distinct cost patterns between patients with de novo stage IV, recurrent <1-year, and recurrent ≥1-year cancer, suggesting unique care trajectories that may influence resource use and planning. Future cost studies among patients with advanced cancer should account for de novo versus recurrent diagnoses and timing of recurrence to obtain estimates that accurately reflect these care pattern complexities.     

Assessing the Effects of Geniculate Artery Embolization in a Nonsurgical Animal Model of Osteoarthritis

PurposeTo create a nonsurgical animal model of osteoarthritis (OA) to evaluate the effects of embolotherapy during geniculate artery embolization (GAE).Materials and MethodsFluoroscopy-guided injections of 700 mg of sodium monoiodoacetate were performed into the left stifle in 6 rams. Kinematic data were collected before and after induction. At 10 weeks after induction, Subjects 1 and 4–6 underwent magnetic resonance (MR) imaging with dynamic contrast enhancement (DCE) and Subjects 1, 3, and 4–6 underwent angiography with angiographic scoring to identify regions with greatest disease severity for superselective embolization (75–250-μm microspheres). Target vessel size was measured. At 24 weeks after angiography, DCE-MR imaging, angiography, and euthanasia were performed, and bilateral stifles were harvested. Medial/lateral tibial and femoral condylar, patellar, and synovial samples were cut, preserved, decalcified, and scored using the Osteoarthritis Research Society International criteria. The stifle and synovium Whole-Organ Magnetic Resonance Imaging Score and Multicenter Osteoarthritis Study score were determined. The volume transfer constant (K_trans) and extracellular volume fraction (v_e) were calculated from DCE-MR imaging along the lateral synovial regions of interest.ResultsThe mean gross and microscopic pathological scores were elevated at 38 and 61, respectively. Mean synovitis score was elevated at 9.2. Mean pre-embolization and postembolization angiographic scores were 5 and 3.8, respectively. Mean superior, transverse, and inferior geniculate artery diameters were 3.1 mm ± 1.21, 2.0 mm ± 0.50, and 1.6 mm ± 0.41 mm, respectively. Mean pre-embolization and postembolization cartilage and synovitis scores were elevated at 35.13 and 73.3 and 5.5 and 9.2, respectively. The K_trans/v_e values of Subjects 4, 5, and 6 were elevated at 0.049/0.38, 0.074/0.53, and 0.065/0.51, respectively. Altered gait of the hind limb was observed in all subjects after induction, with reduced joint mobility. No skin necrosis or osteonecrosis was observed.ConclusionsA nonsurgical ovine animal knee OA model was created, which allowed the collection of angiographic, histopathological, MR imaging, and kinematic data to study the effects of GAE.     

乳腺对比增强能谱X线摄影的研究现状北大核心

对比增强能谱X线摄影(contrast enhancement spectral mammography, CESM)是在普通X线检查的基础上结合血管造影剂进行检查的一种新技术,是通过注射造影剂有效消除组织重叠对病灶的遮盖,实现对肿块真实形态显示,并提供血供信息。目前临床上对CESM进行了一系列的研究,本文就CESM成像原理、对乳腺癌的诊断、应用前景几个方面进行综述。     

Diagnostic pitfalls in needle core biopsy of the breast

Breast core biopsies are a standard component of the triple approach that includes clinical examination, imaging and tissue sampling. Conventional cores, diagnostic vacuum assisted biopsy and vacuum assisted excisions are established methods for sampling and managing breast lesions. It is important to be aware of the potential pitfalls in the technical handling and interpretation of the limited core biopsy samples. Here, we present a clinically oriented, well illustrated overview of the common diagnostic pitfalls based on the author's diagnostic and second opinion practice, emphasize the value of clinicopathological correlation and provide histological tips and clues with useful immunohistochemistry to aid the reporting pathologists in their daily interpretation of breast core biopsies.     

Managing the Breast Cancer Survivor in Primary Care

Early detection and improved treatments for breast cancer are increasing survival rates, thereby growing the number of survivors to almost 4 million in the United States. Continuing care after treatment is frequently provided in primary care. Evidence-based care includes a history review, physical examination, and imaging for recurrence and new cancers, along with health maintenance and health promotion. Additionally, survivors often experience long-term side effects from their disease and/or treatment, affecting health and quality of life. This article provides a synopsis of breast cancer treatment-related side effects and current evidence-based guidelines to assist the primary care provider caring for survivors.     

Automatic segmentation of thoracic CT images using three deep learning models

PurposeDeep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. Image segmentation using DL has given very promising results. In this work, we present and compare the results of segmentation of OARs and a clinical target volume (CTV) in thoracic CT images using three DL models.Materials and methodsWe used CT images of 52 patients with breast cancer from a public dataset. Automatic segmentation of the lungs, the heart and a CTV was performed using three models based on the U-Net architecture. Three metrics were used to quantify and compare the segmentation results obtained with these models: the Dice similarity coefficient (DSC), the Jaccard coefficient (J) and the Hausdorff distance (HD).ResultsThe obtained values of DSC, J and HD were presented for each segmented organ and for the three models. Examples of automatic segmentation were presented and compared to the corresponding ground truth delineations. Our values were also compared to recent results obtained by other authors.ConclusionThe performance of three DL models was evaluated for the delineation of the lungs, the heart and a CTV. This study showed clearly that these 2D models based on the U-Net architecture can be used to delineate organs in CT images with a good performance compared to other models. Generally, the three models present similar performances. Using a dataset with more CT images, the three models should give better results.