Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning

Walker H. Land, Dan Margolis, Ronald Gottlieb, Jack Y. Yang, Elizabeth A. Krupinski

Research output: Contribution to journalShort survey

2 Scopus citations

Abstract

To establish radiologic imaging as a valid biomarker for assessing the response of cancer to different treatments. We study patients with metastatic colorectal carcinoma to learn whether Statistical Learning Theory (SLT) improves the performance of radiologists using Computer Tomography (CT) in predicting patient treatment response to therapy compared with traditional Response Evaluation Criteria in Solid Tumours (RECIST) standard. Preliminary research demonstrated that SLT algorithms can address questions and criticisms associated with both RECIST and World Health Organization (WHO) scoring methods. We add tumour heterogeneity, shape, etc., obtained from CT or MRI scans the feature vector for processing.

Original languageEnglish (US)
Pages (from-to)15-18
Number of pages4
JournalInternational Journal of Computational Biology and Drug Design
Volume3
Issue number1
DOIs
StatePublished - Aug 2010

Keywords

  • Radiological imaging RECIST and WHO measurement methodologies
  • SLT
  • Statistical learning theory

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications

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