Cancer characterization and feature set extraction by discriminative margin clustering

BACKGROUND: A central challenge in the molecular diagnosis and treatment of cancer is to define a set of molecular features that, taken together, distinguish a given cancer, or type of cancer, from all normal cells and tissues. RESULTS: Discriminative margin clustering is a new technique for analyzing high dimensional quantitative datasets, specially applicable to gene expression data from microarray experiments related to cancer. The goal of the analysis is find highly specialized sub-types of a tumor type which are similar in having a small combination of genes which together provide a unique molecular portrait for distinguishing the sub-type from any normal cell or tissue. Detection of the products of these genes can then, in principle, provide a basis for detection and diagnosis of a cancer, and a therapy directed specifically at the distinguishing constellation of molecular features can, in principle, provide a way to eliminate the cancer cells, while minimizing toxicity to any normal cell. CONCLUSIONS: The new methodology yields highly specialized tumor subtypes which are similar in terms of potential diagnostic markers

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Raman spectroscopy for guidance of vulvar cancer surgery:A pilot study

For vulvar squamous cell carcinoma (VSCC), the mainstay of treatment is surgical removal with tumour-free margins. Surgeons still operate without objective tools that provide margin-status. This study assesses Raman spectroscopy potentiality for distinguishing ex-vivo VSCC from healthy tissue in 11 patients. Grid-based Raman maps were obtained from processed spectra. Water content and C-H band ratio (2,910-2,966 cm(-1) / 2810-2890 cm(-1)) were calculated per spectrum and used as linear discriminant parameters. Healthy tissue was differentiated from VSCC with 0.90 discriminative power, 0.79 sensitivity and 0.86 specificity.This is an important step towards the development of objective tools for VSCC surgical guidance