Human breast cancer cell enrichment by Dean flow driven microfluidic channels

Microsystem Technologies - Cell separation based on size by microfluidic devices has become a widely studied research area to facilitate the diagnosis of malaria and cancer, in particular....     

Neural network models for breast cancer prognosis

Estimating the risk of relapse for breast cancer patients is necessary, since it affects the choice of treatment. This problem involves analysing data of times to relapse of patients and relating them to prognostic variables. Some of the times to relapse will usually be censored.We investigate various ways of using neural network models to extend traditional statistical models in this situation. Such models are better able to model both non-linear effects of prognostic factors and interactions between them, than linear logistic or Cox regression models. With the dataset used in our study, however, the prediction of the risk of relapse is not significantly improved when using a neural network model. Predicting the risk that a patient will relapse within three years, say, is possible from this data, but not when any relapse will happen.     

Probabilistic neural network for breast cancer classification

Among cancers, breast cancer causes second most number of deaths in women. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis systems have been proposed in the last years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead. In clinical diagnosis, the use of artificial intelligent techniques as neural networks has shown great potential in this field. In this paper, three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer. Decision making is performed in two stages: training the classifiers with features from Wisconsin Breast Cancer database and then testing. The performance of the proposed structure is evaluated in terms of sensitivity, specificity, accuracy and ROC. The results revealed that PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively. MLP was ranked as the second classifier and was capable of achieving 97.80 and 96.34 % classification accuracy for training and validation phases, respectively, using scaled conjugate gradient learning algorithm. However, RBF performed better than MLP in the training phase, and it has achieved the lowest accuracy in the validation phase.     

On-chip cell migration assay for quantifying the effect of ethanol on MCF-7 human breast cancer cells

Ethanol consumption is associated with the risk of breast cancer progression; however, the mechanism of relationship has not yet been fully explained. Research on breast cancer cell migration after ethanol stimulation may give hope for a better understanding of the disease and oncotherapy. Conventional cell migration assays such as Boyden chamber and wound-healing assays are easy to conduct for this purpose; however, these assays have inherent limitations. In this study, we quantified the effect of ethanol on MCF-7 hunam breast cancer cells using a microfluidics-based wound-healing assay. Wounds were prepared by partially digesting a confluent cell sheet using parallel laminar flows in the presence of protease trypsin. The cells at the leading edge of the wound remained intact. Cell image analysis indicates that all the cells cultured in the microdevice took on a good morphology and monolayer growth status. Cell viability assay demonstrates that cell viability decreased with the increase in ethanol concentration and treatment time. For 0, 22, 43, and 65 mmol/l of ethanol, cell viability after being cultured for 24 h was 100%, 99.6%, 99.4%, and 98.4%, respectively. Studying MCF-7 human breast cancer cell migration when treated with different ethanol concentrations revealed that the cell migration distance is directly proportional with ethanol concentration. After being cultured for 24 h at 37°C and 5% CO₂, the maximal cell migration distance was 231, 283, and 332 μm for 22, 43, and 65 mmol/l ethanol, respectively; all results were higher than the blank test (i.e., ethanol-free test, 218 μm). These findings will be beneficial in developing microfluidic device applications for future research on breast tumor therapy in a biomimetic microenvironment and for developing new methods for breast cancer therapy.     

Implementing automated diagnostic systems for breast cancer detection

This paper intends to an integrated view of implementing automated diagnostic systems for breast cancer detection. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of breast cancer. Because of the importance of making the right decision, better classification procedures for breast cancer have been searched. The classification accuracies of different classifiers, namely multilayer perceptron neural network (MLPNN), combined neural network (CNN), probabilistic neural network (PNN), recurrent neural network (RNN) and support vector machine (SVM), which were trained on the attributes of each record in the Wisconsin breast cancer database, were compared. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. This research demonstrated that the SVM achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.     

Robust predictive model for evaluating breast cancer survivability

ObjectiveMany machine learning models have aided medical specialists in diagnosis and prognosis for breast cancer. Accuracy has been regarded as a primary measurement for the performance evaluation of the models, but stability which indicates the robustness of the performance to model parameter variation also becomes essential. A stable model is in practice of benefit to the medical specialists who may have little expertise in model tuning. The main purpose of this work is to address the importance of the stability of a model and to suggest one of such models.MethodsA comparative study of three prominent machine learning models was carried out for the prognosis of breast-cancer survivability: support vector machines, artificial neural networks, and semi-supervised learning models.MaterialThe surveillance, epidemiology, and end results database for breast cancer was used, which is known as the most comprehensive source of information on cancer incidence in the United States.ResultsThe best performance was obtained from the semi-supervised learning model. It showed good overall accuracy and stability under model parameter variation. The sharpening procedure enhanced the stability of the model via the noise-reduction.ConclusionWe suggest that semi-supervised learning model is a good candidate that medical professionals readily employ without consuming the time and effort for parameter searching for a specific model. The ease of use and faster time to results of the predictive model will eventually lead to the accurate and less-invasive prognosis for breast cancer patients.     

A probabilistic model for breast cancer survival data

The aim of the present study is the development of a probabilistic model to analyze breast cancer survival data. A finite-state discrete-time Markov chain model has been formulated for the analysis of follow-up probability and mortality data for 780 breast cancer patients.

The proposed stochastic model can also be used in comparing the transition in order to estimate treatment effects.     

Implementing automated diagnostic systems for breast cancer detection

This paper intends to an integrated view of implementing automated diagnostic systems for breast cancer detection. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of breast cancer. Because of the importance of making the right decision, better classification procedures for breast cancer have been searched. The classification accuracies of different classifiers, namely multilayer perceptron neural network (MLPNN), combined neural network (CNN), probabilistic neural network (PNN), recurrent neural network (RNN) and support vector machine (SVM), which were trained on the attributes of each record in the Wisconsin breast cancer database, were compared. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. This research demonstrated that the SVM achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.     

What is important in electronic textbooks for students of different achievement levels?

This paper reports the findings of a study that explored which characteristics of electronic textbooks correlated with knowledge acquisition by learners of different achievement levels. The study was carried out on 35 units of electronic textbooks that were studied by 19 high-achieving and 19 low-achieving students in four Estonian schools. The low-achieving students profited from clear instructions, familiar icons, examples, and answering from the keyboard. The high-achieving students benefited from key-combinations, menus with different levels, the Internet, analogies and lower density of terms in the content of the material. In electronic textbooks, not only the content, but also the design of the software, should be different for learners with a different achievement level.     

A GAs based approach for mining breast cancer pattern

Data mining usually means the methodologies and tools for the efficient new knowledge discovery from databases. In this paper, a genetic algorithms (GAs) based approach to assess breast cancer pattern is proposed for extracting the decision rules including the predictors, the corresponding inequality and threshold values simultaneously so as to building a decision-making model with maximum prediction accuracy. Early many studies of handling the breast cancer diagnostic problems used the statistical related techniques. As the diagnosis of breast cancer is highly nonlinear in nature, it is hard to develop a comprehensive model taking into account all the independent variables using conventional statistical approaches. Recently, numerous studies have demonstrated that neural networks (NNs) are more reliable than the traditional statistical approaches and the dynamic stress method. The usefulness of using NNs have been reported in literatures but the most obstacle is the in the building and using the model in which the classification rules are hard to be realized. We compared our results against a commercial data mining software, and we show experimentally that the proposed rule extraction approach is promising for improving prediction accuracy and enhancing the modeling simplicity. In particular, our approach is capable of extracting rules which can be developed as a computer model for prediction or classification of breast cancer potential like expert systems.     

A proposed framework for diagnosis and breast cancer detection

Multimedia Tools and Applications - This paper aims to early Breast Cancer (BC) detection by Mammography (MG) established on the production of excellent images and competent interpretation. This...     

Higher order spectra analysis of breast thermograms for the automated identification of breast cancer

Breast cancer is a leading cancer affecting women worldwide. Mammography is a scanning procedure involvingX‐rays of the breast. It causes discomfort and may cause high incidence of false negatives. Breast thermography is a new screening method of breast that helps in the early detection of cancer. It is a non‐invasive imaging procedure that captures the infrared heat radiating off from the breast surface using an infrared camera. The main objective of this work is to evaluate the use of higher order spectral features extracted from thermograms in classifying normal and abnormal thermograms. For this purpose, we extracted five higher order spectral features and used them in a feed‐forward artificial neural network (ANN) classifier and a support vector machine (SVM). Fifty thermograms (25 each of normal and abnormal) were used for analysis.SVM presented a good sensitivity of 76% and specificity of 84%, and theANN classifier demonstrated higher values of sensitivity (92%) and specificity (88%). The proposed system, therefore, shows great promise in automatic classification of normal and abnormal breast thermograms without the need for subjective interpretation.     

Ultrasound based application for intraglandular mapping of breast cancer

Purpose

Breast cancer is the most common malignant tumor among women worldwide. Breast cancer is one of the few cancers that can be early detected, and the survival rate of the women whose breast cancers are detected on their initial stage is virtually 100%. At the present time, ultrasound (US) is the most important imaging test together with the mammogram for the diagnostic evaluation of the breast. Recent studies have shown that ultrasound, in addition to mammography, helps doctors to spot significantly more cancers compared with mammograms alone.This work intends to standardize the process of the US breast examination, the storage and marking of the US images and their subsequent visualization and comparison.

Methods

It presents an innovative technique for the intraglandular mapping of breast cancer in a 3D scene. An anatomical based model of the breast is used for storage of the US images. Hardware equipment needed for the breast examination is described. Soft application programmed on Apple tools is fully described. The database for the storage is presented.

Results

First clinical applications of the presented tool are reported. Currently, the system is being distributed free of charge to clinical personal in order to evaluate its benefits.

Conclusions

A first version of an application to standardize the process of the US breast examination is presented. First reports show the feasibility of the system to be applied on clinics.     

Evidence-based recommendations for building better bras for women treated for breast cancer

Participating in exercise is beneficial for women who have been treated for breast cancer. However, not being able to find a comfortable exercise bra can be a barrier to exercise participation. This study aimed to systematically investigate what breast support women treated for breast cancer want when they exercise in order to provide evidence-based recommendations to improve exercise bra designs for these women. Based on 432 responses from a national online survey, frequency and relationship data were analysed (binary logistic regression) to understand exercise bra issues pertinent to this population. These issues included being able to control for asymmetrical cup sizes, managing heightened skin sensitivity, managing fluid (size) fluctuations, managing a prosthesis and restoring body image by restoring shape. This study provides evidence-based recommendations to inform an exercise bra design that will meet the unique needs of women treated for breast cancer. Rigorous, evidence-based evaluations of exercise bras for women treated for breast cancer may contribute to their well-being and quality of life through enhanced designs.     

新型检测癌细胞的谐振式生物传感器

研究了一种新型利用磁微球的间接检测癌细胞的生物传感器。利用磁微球分离细胞,将分离细胞过程和检测过程分开,避免了现有方法制作敏感膜的步骤。实验结果表明:这种检测方法操作简单、快速,具有明显的灵敏性和选择性。     

A fuzzy information-based approach for breast cancer risk factors assessment

The aim of this study is to define the risk factors that are effective in Breast Cancer (BC) occurrence, and to construct a supportive model that will promote the cause-and-effect relationships among the factors that are crucial to public health. In this study, we utilize Rule-Based Fuzzy Cognitive Map (RBFCM) approach that can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause-and-effect relationships among the concepts to model the behavior of any system. In this study, a decision-making system is constructed to evaluate risk factors of BC based on the information from oncologists. To construct causal relationship, the weight matrix of RBFCM is determined with the combination of the experts’ experience, expertise and views. The results of the proposed methodology will allow better understanding into several root causes, with the help of which, oncologists can improve their prevention and protection recommendation. The results showed that Social Class and Late Maternal Age can be seen as important modifiable factors; on the other hand, Benign Breast Disease, Family History and Breast Density can be considered as important factors as non-modifiable risk factors. This study is somehow weighing the interrelations of the BC risk factors and is enabling us to make a sensitivity analysis between the scenario studies and BC risk factors. A soft computing method is used to simulate the changes of a system over time and address “what if” questions to compare between different case studies.     

BREASTCAN: an expert system for postoperative breast cancer therapy

An expert system, named BREASTCAN and designed to assist physicians giving postoperative adjuvant chemotherapy for breast cancer, is described. The system is based on frames, each corresponding to one stage of treatment--either a decision-making stage or a therapeutic stage. The system has been designed to allow fast and easy consultation by general practitioners lacking computer knowledge.     

Hierarchical multiresolution approaches for dense point-level breast cancer treatment data

The analysis of point-level (geostatistical) data has historically been plagued by computational difficulties, owing to the high dimension of the nondiagonal spatial covariance matrices that need to be inverted. This problem is greatly compounded in hierarchical Bayesian settings, since these inversions need to take place at every iteration of the associated Markov chain Monte Carlo (MCMC) algorithm. This paper offers an approach for modeling the spatial correlation at two separate scales. This reduces the computational problem to a collection of lower-dimensional inversions that remain feasible within the MCMC framework. The approach yields full posterior inference for the model parameters of interest, as well as the fitted spatial response surface itself. We illustrate the importance and applicability of our methods using a collection of dense point-referenced breast cancer data collected over the mostly rural northern part of the state of Minnesota. Substantively, we wish to discover whether women who live more than a 60-mile drive from the nearest radiation treatment facility tend to opt for mastectomy over breast conserving surgery (BCS, or “lumpectomy”), which is less disfiguring but requires 6 weeks of follow-up radiation therapy. Our hierarchical multiresolution approach resolves this question while still properly accounting for all sources of spatial association in the data.     

基于支持向量机的乳腺癌化疗预后状态预测

乳腺癌是危害女性生命的一种恶性肿瘤。目前,在乳腺癌治疗方面,新辅助化疗获得了良好的成果,使众多女性恢复了健康。支持向量机在实际应用中有着良好的泛化和学习能力,并在商业、经济以及医疗等领域有所应用。采用决策树分类器和支持向量机分类器,结合乳腺癌新辅助化疗随访记录数据,预测乳腺癌患者新辅助化疗的预后状态,实验结果表明使用支持向量机的效果好于使用决策树的效果,在支持向量机中使用径向基核函数时获得了最高的准确率,达到了84.08%,由此可见,该分类方法可能成为一种乳腺癌新辅助化疗的预后状态的有效预测工具。