An integrative approach for in silico glioma research

Lee A D Cooper, Jun Kong, David A. Gutman, Fusheng Wang, Sharath R. Cholleti, Tony C. Pan, Patrick M. Widener, Ashish Sharma, Tom Mikkelsen, Adam E. Flanders, Daniel L. Rubin, Erwin G. Van Meir, Tahsin M. Kurc, Carlos S. Moreno, Daniel J. Brat, Joel H. Saltz

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors,where themorphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.

Original languageEnglish (US)
Pages (from-to)2617-2621
Number of pages5
JournalIEEE Transactions on Biomedical Engineering
Issue number10 PART 2
StatePublished - Oct 2010


  • Biology
  • Brain tumor
  • Image analysis
  • In silico
  • Microscopy

ASJC Scopus subject areas

  • Biomedical Engineering


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