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The ability to selectively detect and target cancer cells that have undergone an epithelial-mesenchymal transition (EMT) may lead to improved methods to treat cancers such as pancreatic cancer. The remodeling of cellular glycosylation previously has been associated with cell differentiation and may represent a valuable class of molecular targets for EMT.
Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.
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