原发性乳腺弥漫性大B细胞淋巴瘤5例并文献复习

目的探讨原发性乳腺弥漫性大B细胞淋巴瘤(primary breast diffuse large B-cell lymphoma,PB-DLBCL)的临床病理学特点、诊断及鉴别诊断、治疗及预后。方法采用HE染色和免疫组织化学SP法分析5例PB-DLBCL患者的临床表现、病理学及免疫表型特征,并复习相关文献。结果 5例均为女性患者,发病年龄48~70岁,中位年龄59.2岁,均为单侧乳腺肿块,其中左乳3例,右乳2例。镜下见乳腺正常结构被破坏,成片的中等偏大的肿瘤细胞弥漫浸润乳腺小叶、导管周围、间质及周围脂肪组织。根据免疫组织化学表型,4例为非生发中心型,1例为生发中心型;Ki67增殖指数为60%~90%;根据 Ann Arbor 分期标准,5例均为II E期。病例随访时间截止至2018年6月,随访期内,有2例复发,分别于7和19个月后死亡;2例健在,分别已存活12和72个月;另外一例失访。结论 PB-DLBCL是一种少见的恶性淋巴瘤,临床表现为单侧乳腺无痛性包块进行性增大,以右侧多见。确诊主要依靠病理活检及免疫表型,免疫表型以非生发中心为主,以化疗联合放疗等综合治疗方案为宜。     

Stress distribution of three-unit fixed partial prostheses (conventional and pontic) supported by three or two implants: 3D finite element analysis of ductile materials

In implantology, when financial or biological feasibility limitations appear, it is necessary to use prostheses with geometries that deviate from the conventional, with a pontic in the absence of an intermediate implant. The aim of this study was analyze and understand the general differences in the stresses generated in implants, components and infrastructures according to the configuration of the prosthesis over three or two implants. Thus, this paper analyzes the von Mises equivalent stresses (VMES) of ductile materials on their external surfaces. The experimental groups: Regular Splinted Conventional Group (RCG), which had conventional infrastructures on 3 regular-length Morse taper implants (4x11?mm); Regular Splinted Pontic Group (RPG), which had infrastructures with intermediate pontics on 2 regular-length Morse taper implants (4x11?mm). The simulations of the groups were created with Ansys Workbench 10.0 software. The results revealed that the RPG presented greater areas of possible fragility due to higher stress concentrations, for example, in the cervical area of the union between the implant and component the top platform of the abutment, as well as greater coverage of the stress by the cervical implant threads. The RPG infrastructure was also more affected by stresses in the connection areas between the prostheses and on the occlusal surface. There is an advantage to using prostheses supported by a greater number of implants (RCG) because this decreases the stress in the analyzed structures and consequently improves stress dissipation to the supporting bone, which would preserve the system.     

Breast cancer incidence by country of birth among immigrant women in British Columbia,Canada

IntroductionBreast cancer rates vary internationally and between immigrant and non-immigrant populations. We describe breast cancer incidence by birth region and country in British Columbia, Canada.MethodsWe linked population-based health and immigration databases for a population with >1.29 million immigrants to assess breast cancer incidence among immigrant and non-immigrant women. We report age-standardized incidence ratios (SIRs) by birth region and country using non-immigrant women as the standard.ResultsSIRs varied widely by both birth country and region. Low rates were found for South (SIR = 0.52, 95% CI: 0.47,0.59) and East Asian (SIR = 0.75, 95% CI: 0.72,0.79) women and a higher rate for Western Europeans (SIR = 1.15, 95% CI: 1.01,1.30).ConclusionThere is considerable variation in SIRs across some of British Columbia’s largest immigrant populations and several demonstrate significantly different risk profiles compared to non-immigrants. These findings provide unique data to support breast cancer prevention and control.     

Determination of the Blood,Hormone and Obesity Value Ranges that Indicate the Breast Cancer,Using Data Mining Based Expert System

Breast cancer is a dangerous type of cancer that spreads into other organs over time. Therefore, medical studies are being done for the early diagnosis by means of the anthropometric data and blood analysis values besides the mammographic and histological findings. However, medical studies have identified only cancer-related values but the value ranges indicating the cancer have not been determined yet. Concurrently the automated diagnostic systems are being developed to assist medical specialists in biomedical engineering studies. The range of values or boundaries indicating the cancer are automatically determined in biomedical methods, but only the diagnostic result is presented. Because of this, biomedical studies don't provide enough opportunity for medical experts to evaluate the relationship between values and result. In this study, decision trees that is one of data mining method was applied to anthropometric data and blood analysis values to complete the mentioned deficiencies in breast cancer diagnosis aiming studies. The determined value ranges were also presented visually to medical experts understand them easily. The proposed diagnostic system has accuracy rate up to 90.52% and provides value ranges indicating the breast cancer as well as mathematically presents the relations between the values and cancer.     

Risk factors for breast cancer and their association with molecular subtypes in a population of Northeast Brazil

BackgroundThe risk factors for breast cancer (BC) among women in Brazilian populations are poorly understood. To date, few Brazilian studies have addressed the potential association between risk factors and molecular BC subtypes. This case-control study aimed to identify risk factors for BC in a population of Northeast Brazil.MethodsData from 313 patients with invasive BC and 321 healthy controls were obtained from medical records from two cancer treatment centres and personal interviews. Of the 313 BC patients, 224 (71.6%) had reached menopause. The following distribution of subtypes was found among 301 patients: (1) Luminal A: 54 (17.9%); (2) Luminal B: 175 (58.1%); (3) HER2/neu: 29 (9.7%); and (4) triple-negative breast cancer (TNBC): 43 (14.3%). Odds ratios (ORs) and confidence intervals (CIs) were determined using regression analysis.ResultsRegression modelling indicated that family history, obesity (≥ 30.0 kg/m²), alcohol consumption and contraceptive use increased the overall risk of BC 1.78 (95% CI: 1.22–2.59), 1.69 (95% CI: 1.08–2.63), 2.21 (95% CI: 1.44–3.39) and 2.99 (95% CI: 2.09–4.28) times, respectively. After stratification for menopausal status, alcohol consumption increased the risk of BC 4.15 (95% CI: 2.13–8.11) times, and obesity, as a single variable, increased the risk of BC 2.02 (95% CI: 1.22–3.37) times, only among postmenopausal women. In a case-control analysis, the risk of TNBC and Luminal B breast cancer were 4.06 (95% CI: 1.58–10.42) and 1.87 times (95% CI: 1.13–3.11) higher, respectively, in obese women than in non-obese women. Furthermore, alcohol consumption increased the risk of Luminal A and B subtypes 7.08 (3.40–14.73) and 1.77 (1.07–2.92) times, respectively.ConclusionFamily history, contraceptive use, obesity and alcohol consumption increased the risk of BC. Obesity and alcohol consumption differentially increased risk of TNBC and Luminal molecular subtypes.     

Changes in cigarette smoking behavior among breast cancer and unaffected women – A prospective study in the MARIE cohort

BackgroundSmoking cessation after a cancer diagnosis can reduce adverse cancer treatment outcomes. Whether a breast cancer diagnosis, a cancer commonly seen as unrelated to smoking cigarettes, motivates changes in smoking behavior is not fully understood. We aimed to compare long-term changes at three follow-up times of cigarette smoking behavior in women with breast cancer and baseline age- and region-matched unaffected women.MethodsWe used longitudinal data from the population-based case-control study MARIE (Mamma Carcinoma Risk Factor Investigation). Women with breast cancer (N = 3813) and unaffected women (N = 7341) aged 50–74 years were recruited from 2002 to 2005. Analyses on changes in smoking were based on data from those who also completed follow-up 1 in 2009–2012, follow-up 2 in 2014–2016 and follow-up 3 in 2020. Multinomial logistic regression for changes (quitting, stable, or start smoking) adjusted for age, study region, education, comorbidities, living situation, and follow-up time, was applied to examine the associations between breast cancer status and changes in smoking behavior.ResultsWomen with breast cancer had significantly higher odds than unaffected women of quitting smoking (OR = 1.38, 95 % CI: 1.01–1.89) and lower odds of returning to smoking (OR = 0.29, 95 % CI: 0.09–0.94) at follow-up 1, but were more likely to start or return to smoking at follow-up 2 (OR = 2.11, 95 % CI 1.08–4.15). No significant group differences were found for changes in smoking behavior at follow-up 3.ConclusionOur findings indicate that short-term changes in smoking behavior can be attributed to a breast cancer diagnosis, but that over time the effect diminishes and changes in smoking no longer differ between breast cancer and breast cancer-free women. To support smoking cessation and to prevent relapse, guidelines to address smoking in cancer care, as well as comprehensive tobacco treatment services, are needed.     

Deep Learning-Based Multi-Label Tissue Segmentation and Density Assessment from Mammograms

Objectives: Breast cancer is the most commonly diagnosed type of cancer among women and a common cause of cancer-related deaths. Early diagnosis and treatment of breast cancer is critical in disease prognosis. Breast density is known to have a correlation with breast cancer. In recent years, there has been an increasing interest in the investigation of computer-aided methods for early diagnosis of breast cancer. In this study, a new fully-automated deep learning-based cascaded model was proposed for breast density assessment. In the first stage, the segmentation of adipose, fibroglandular, and pectoral muscle tissues from the digitized film mammograms of the Digital Database for Screening Mammography (DDSM) was investigated using various types of U-nets. Features extracted from the breast tissue segmentation predictions were then used to assess breast density in the second stage. Material and methods: 66 and 296 mediolateral oblique mammograms were selected from DDSM dataset for segmentation and breast density assessment systems, respectively. Different U-nets with varying number of layers and filters were implemented and the model having the highest performance was determined. U-net performance was investigated using categorical cross-entropy, Dice, Tversky, Focal Tversky, and logarithmic cosine-hyperbolic Dice loss functions. The performances of U-nets having different types of connections were investigated. The performances of U-nets having pre-trained weights from VGG16, VGG19, and ResNet50 networks in the encoding path were also investigated. Segmentation results were improved by using an image processing pipeline based on morphological operators. Segmentation performance was presented in terms of accuracy, balanced accuracy, intersection over union, and Dice's similarity coefficient (DSC) metrics. The segmentation system predictions were then used to estimate mammographic density using a machine learning pipeline by extracting features related to the fibroglandular tissue percentage. Results: Using ResNet50-U-net on the test data, average DSC scores of 82.71%, 73.39%, and 95.30% were obtained for adipose, fibroglandular, and pectoral muscle tissue segmentation, respectively. The mammogram segmentation results are 3%-12% better than the current state-of-the-art DSC in the literature when considering all of the foreground tissues concurrently. A breast density classification accuracy of 76.01% was achieved on a separate mammogram dataset, which is comparable to the recent studies in the literature. Conclusion: The proposed system can be used for automatic segmentation of mammogram into adipose, fibroglandular, and pectoral muscle tissues. The segmentation model enables the estimation of the fibroglandular-adipose tissue interface, which is recently found to be an important region for breast cancer investigations. The proposed fully-automatic breast density assessment system has a comparable performance to the ones in the literature.     

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

BackgroundThe prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes.DatasetWe used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCAMethodsIn this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance.ResultsThe results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.     

The role of microbicidal lipids in host defense against pathogens and their potential as therapeutic agents

Lipids such as fatty alcohols, free fatty acids and monoglycerides of fatty acids are known to be potent antimicrobial/microbicidal agents in vitro and to kill enveloped viruses, Gram-positive and Gram-negative bacteria and fungi on contact. For over half a century several studies have tried to answer the question of whether or not lipids play a role in the natural host defense against pathogens. A comprehensive review is given of these studies, particularly concerning infections in skin and in mucosal membranes of the respiratory tract, and of the role of lipids in the antimicrobial activity of breast milk. Based on studies of the microbicidal activities of lipids, both in vitro and in vivo, the possibility of using such lipids as active ingredients in prophylactic and therapeutic dosage forms is considered and examples are given of studies of such pharmaceutical dosage forms in experimental animal models and in clinical trials.