Computerized breast cancer diagnosis and prognosis from fine-needle aspirates

Identification of New Clusters from Labeled Data Using Mixture Models

Nowadays attempts to segment classes or groups are often found in various fields. Especially, one of emerging issues in biological and medical areas is identification of new subtypes of biological samples or patients. For the identification, we often need to find new subtypes from known classes. In such cases, we usually use clustering techniques. However, usual clustering methods could mix up the labels of the known classes in clustering outcomes and it might lead to wrong interpretation for the identified clusters. Also, they do not use the information about known classes. Thus, this study proposes a Gaussian mixture model-based approach for identifying new clusters from known classes while it maintains them. The performance of the proposed model is verified through simulations and it is applied to a breast cancer data set.

On what to permute in test-based approaches for variable importance measures in Random Forests

MOTIVATION

In bioinformatics applications, it is currently customary to permute the outcome variable in order to produce inference on covariates to test novel methods or statistics whose distributions are poorly known. The seminal publication of Altmann et al. in Bioinformatics uses the same permutation scheme to obtain P-values that can be treated as corrected measure of feature importance to rectify the bias of the Gini variable importance in Random Forests. Since then, such method has been used in applied work to also draw statistical conclusions on variable importance measures from resulting P-values.

RESULTS

In this paper, we show that permuting the outcome may produce unexpected results, including P-values with undesirable properties and illustrate how more refined permutation schemes can be appropriate to obtain desirable results, including high power in discovering relevant variables.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer

BACKGROUND Breast cancer is one of the most common cancer types in the world and is a serious threat to health. This type of cancer is complex; it is a hereditary disease and does not result from a single cause. The diagnosis of cancer starts with a biopsy. Various methods are used to detect and recognize cancer cells, from microscopic images and mammography to ultrasonography and magnetic resonance images (MRI). MATERIAL AND METHODS Detection and characterization of benign and malignant cells by image-processing-based segmentation for breast cancer diagnosis is important for early diagnosis. In the present study, Extreme Learning Machine (ELM) classification was performed for 9 features based on image segmentation in the Breast Cancer Wisconsin (Diagnostic) data set in the UC Irvine Machine Learning Repository database. RESULTS The results obtained with the developed method were compared with the results of other machine learning methods (Naive Bayes, Support Vector Machine, and Artificial Neural Network) and it showed the highest performance, with a result of 98.99%. CONCLUSIONS It was found that both accuracy and speed were good. We present a method that can be applied in cell morphology detection and classification in automated systems that classify by computer-aided mammogram image features.

Breast tissue image classification based on Semi-supervised Locality Discriminant Projection with Kernels

Breast tissue classification is an important and effective way for computer aided diagnosis of breast cancer. We present Semi-supervised Locality Discriminant Projections with Kernels for breast cancer classification. The contributions of this work lie in: 1) Semi-supervised learning is used into Locality Preserving Projections (LPP) to enhance its performance using side-information together with the unlabelled training samples, while current algorithms only consider the side-information but ignoring the unlabeled training samples. 2) Kernel trick is applied into Semi-supervised LPP to improve its ability in the nonlinear classification. 3) The framework of breast cancer classification with Semi-supervised LPP with kernels is presented. Many experiments are implemented on four breast tissue databases to testify and evaluate the feasibility and affectivity of the proposed scheme.

Nonlinear knowledge in kernel approximation

Prior knowledge over arbitrary general sets is incorporated into nonlinear kernel approximation problems in the form of linear constraints in a linear program. The key tool in this incorporation is a theorem of the alternative for convex functions that converts nonlinear prior knowledge implications into linear inequalities without the need to kernelize these implications. Effectiveness of the proposed formulation is demonstrated on two synthetic examples and an important lymph node metastasis prediction problem. All these problems exhibit marked improvements upon the introduction of prior knowledge over nonlinear kernel approximation approaches that do not utilize such knowledge.

Quantitative morphometric analysis of fine needle aspirates of breast carcinoma

BACKGROUND

Reports of morphometric analysis using computerized digitized images of fine needle aspirates of breast cancer are rare. We aimed to evaluate whether quantitative morphometric estimations of cytological nuclear features could be used for preoperative prediction of the risk of recurrence as well as chemosensitivity.

METHODS

Seventy consecutive breast cancer patients were treated with preoperative chemotherapy (Doxifluridine 800 mg/body/day, orally) for 4 weeks and subsequently they underwent surgery. Using Papanicolaou-stained cytological materials, computerized morphometric analyses were performed. Mean nuclear area (NA) was measured and the coefficient of variation of NA (NACV) was calculated as the quantitative parameter of nuclear atypism. NA and NACV were compared with prognostic factors (tumor size, histological grade, hormone receptor status, nodal status, and ploidy status), and with the response to the chemotherapy.

RESULTS

NA and NACV were significantly associated with hormone receptor status and ploidy status (p<0.05). NACV correlated with histological grade (p<0.05). Neither NA nor NACV were associated with tumor size and nodal status. Patients with high NACV (>35%) had lower rates of disease-free survival (p<0.05) than those with low NACV (< or =35%). Responders to preoperative chemotherapy had statistically larger NA, higher NACV and higher S-phase fraction at the time of diagnosis compared with non-responders (p<0.001, p<0.0005, and p<0.05 respectively).

CONCLUSIONS

Morphometric analysis of preoperative fine needle aspirates reflects important clinical information, such as the risk of recurrence, and particularly, chemosensitivity.

Model selection for a medical diagnostic decision support system: a breast cancer detection case

There are a number of different quantitative models that can be used in a medical diagnostic decision support system (MDSS) including parametric methods (linear discriminant analysis or logistic regression), non-parametric models (K nearest neighbor, or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. Practitioners are left to either choose a favorite model or to test a small subset using cross validation methods. This paper illustrates the use of a self-organizing map (SOM) to guide model selection for a breast cancer MDSS. The topological ordering properties of the SOM are used to define targets for an ideal accuracy level similar to a Bayes optimal level. These targets can then be used in model selection, variable reduction, parameter determination, and to assess the adequacy of the clinical measurement system. These ideas are applied to a successful model selection for a real-world breast cancer database. Diagnostic accuracy results are reported for individual models, for ensembles of neural networks, and for stacked predictors.

Computer-derived nuclear features compared with axillary lymph node status for breast carcinoma prognosis

BACKGROUND

Both axillary lymph node involvement and tumor anaplasia, as expressed by visually assessed grade, have been shown to be prognostically important in breast carcinoma outcome. In this study, axillary lymph node involvement was used as the standard against which prognostic estimations based on computer-derived nuclear features were gauged.

METHODS

The prognostic significance of nuclear morphometric features determined by computer-based image analysis were analyzed in 198 consecutive preoperative samples obtained by fine-needle aspiration (FNA) from patients with invasive breast carcinoma. A novel multivariate prediction method was used to model the time of distant recurrence as a function of the nuclear features. Prognostic predictions based on the nuclear feature data were cross-validated to avoid overly optimistic conclusions. The estimated accuracy of these prognostic determinations was compared with determinations based on the extent of axillary lymph node involvement.

RESULTS

The predicted outcomes based on nuclear features were divided into three groups representing best, intermediate, and worst prognosis, and compared with the traditional TNM lymph node stratification. Nuclear feature stratification better separated the prognostically best from the intermediate group whereas lymph node stratification better separated the prognostically intermediate from the worst group. Prognostic accuracy was not increased by adding lymph node status or tumor size to the nuclear features.

CONCLUSIONS

Computer analysis of a preoperative FNA more accurately identified prognostically favorable patients than did pathologic examination of axillary lymph nodes and may obviate the need for routine axillary lymph node dissection.