Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging

Gabriela Alexe, James Monaco, Scott Doyle, Ajay Basavanhally, Anupama Reddy, Michael Seiler, Shridar Ganesan, Gyan Bhanot, Anant Madabhushi

Research output: Contribution to journalShort surveypeer-review

25 Scopus citations


With the increasing cost effectiveness of whole slide digital scanners, gene expression microarray and SNP technologies, tissue specimens can now be analyzed using sophisticated computer aided image and data analysis techniques for accurate diagnoses and identification of prognostic markers and potential targets for therapeutic intervention. Microarray analysis is routinely able to identify biomarkers correlated with survival and reveal pathways underlying pathogenesis and invasion. In this paper we describe how microarray profiling of tumor samples combined with simple but powerful methods of analysis can identify biologically distinct disease subclasses of breast cancer with distinct molecular signatures, differential recurrence rates and potentially, very different response to therapy. Image analysis methods are also rapidly finding application in the clinic, complementing the pathologist in quantitative, reproducible, detection, staging, and grading of disease. We will describe novel computerized image analysis techniques and machine learning tools for automated cancer detection from digitized histopathology and how they can be employed for disease diagnosis and prognosis for prostate and breast cancer.

Original languageEnglish (US)
Pages (from-to)860-879
Number of pages20
JournalExperimental Biology and Medicine
Issue number8
StatePublished - Aug 1 2009

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)


  • Clinically relevant tumor subtypes
  • Computerized image analysis
  • Diagnostic and prognostic markers
  • Microarray analysis of tumors
  • PCA and consensus ensemble clustering

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