Breast Cancer: Advancement in Diagnostic and Treatment

Breast Cancer: Advancement in Diagnostic and Treatment

Yos S. Morsi (Swinburne University of Technology, Australia), Pujiang Shi (Swinburne University of Technology, Australia) and Amal Ahmed Owida (Swinburne University of Technology, Australia)
DOI: 10.4018/978-1-61692-004-3.ch009

Abstract

Breast cancer is the second most common cancer in the world and is difficult to accurately identify and treat. Diagnostic computational tools can be used effectively, with high degree of accuracy, to recognize and differentiate between the two known types of breast lesion, namely benign and malignant. These modelling tools include artificial intelligence techniques such as Artificial Neural Networks (ANNs), Fuzzy Logic (FL), Hidden Markov Model (HMM) and Support Vector Machines (SVMs). These tools can identify the important features that play pivotal roles in the classification task, and can aid physicians to diagnose and prognosticate breast cancer. Moreover, recent advancement in nanotechnology indicates that with the aid of nanoparticles, nanowires, nanorobots and nanotubes, the disese of breast cancer can be potentially eradicated totally. The chapter highlights the limitations of the current therapies used in breast cancer and discusses the concept of nanotechnology as a possible future therapy.
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Diagnosis And Modeling Techniques

It is well recognized that in treatment and diagnostic of breast cancer, clinicians are often presented with identical clinical information. However, unfortunately clinicians can act in different ways depending on their knowledge and experience which highlighted the need to introduce diagnostic tools to support the scientific homogeneity and accountability of healthcare decisions and actions. The benefits expected from such actions include an overall reduction in cost, improved quality of care as well as patient and public opinion satisfaction. Recently, computer-based medical data processing research has yielded methods and tools for managing the task away from the hospital management level and closer to the desired disease and patient management level.

In search of an accurate tool for distinguish and diagnostic cancer lesions various techniques have been proposed. Recent research focuses primarily on the application of computer vision and for early lesion identification in mammograms and breast-imaging volumes through computer-aided diagnostics (CAD) tools with particular emphasis on computational diagnostics methodology for the analysis of molecular disease mechanisms in cancer. To this end, advanced applications of information and disease process modeling technologies have already demonstrated an ability to significantly augment clinical decision making. One of the main obstacles that need to be overcome is the development of systems that treat both information and knowledge as clinical objects with same modeling requirements. Here we briefly describe some of the existing computational models used for classification of the types of breast lesion in brief here.

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