PATHOLOGY VISIONS 2014 PRESENTER
  


Bahram Parvin, PhD

University of Nevada Reno, Lawrence Berkeley Laboratory

Presenting:
Predictive Morphometric Subtypes in Glioblastoma Multiforme


Abstract: Traditional approach to computational histopathology has focused on computer aided pathology (CAP). In recent years, the field of computational pathology has extended to precision medicine via proliferation of large scale data such as The Cancer Genome Atlas (TCGA). TCGA is a rapidly expanding resource that is accelerating discovery in cancer by providing the research community with mineable genomics and clinical outcome data. Pathology images, from H&E stained samples, have been added to complement the molecular and clinical data. However, utilization of whole slide images is substantially hindered by the batch effects, biological heterogeneity, tumor composition, and complexities of tumor architecture. Computational techniques will be presented that overcome these complexities to reveal intrinsic predictive subtypes from the morphometric signature of a cohort of 250 GBM patients. Molecular correlates of each subtype are then constructed for potential targeted therapy. In addition to computed morphometric subtypes, tumor heterogeneity is also examined to hypothesize the molecular drivers of heterogeneity and whether heterogeneity is more virulent in predicting the outcome.


Biography: Bahram Parvin is the professor of biomedical engineering at the University of Nevada, Reno and a principal scientist at the Lawrence Berkeley National Laboratory. His laboratory focuses on technology development for (i) realization of pathway pathology, (ii) high content screening of multicellular systems, and (iii) molecular transporters in microbial systems. He was the General Chair of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro in 2013, and is the Associate Editor for IEEE Transactions on Medical Imaging. He is also a member of the steering committee for IEEE Bioimaging and Signal Processing as well as IEEE Bioengineering and Health Care Informatics. He led the development of BioSig3D, which received the R&D100 award in 2014.











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