Computer-Aided Diagnosis And Lipidomics Analysis To Detect And Treat Breast Cancer

Multi-modality diagnosis techniques are more and more replacing traditional medical imaging for breast cancer detection. Newly emerging advances in both intelligent cancer detection systems and lipidomics technologies offer an excellent opportunity to detect tumors and to understand regulation at the molecular level in many diseases such as cancer. In this paper, we present a detailed computer-aided diagnosis (CAD) systems combining motion artefact reduction and automated feature extraction and classification, and a novel data mining approach for visualization of gene therapy leading to apoptosis in U87 MG glioblastoma cells, a secondary tumor of breast cancer. The achieved results show that the CAD system represents a robust and integrative tool for reliable small contrast enhancing lesions. Graph-clustering methods are introduced as powerful correlation networks which enable a simultaneous exploration and visualization of co-regulation in glioblastoma data. These new paradigms are providing unique "fingerprints" by revealing how the intricate interactions at the lipidome level can be employed to induce apoptosis (cell death) and are thus opening a new window to biomedical frontiers.