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

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

Addresses: Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA. ' Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA. ' Department of Radiology, University of Arizona, Tucson, AZ 85724, USA. ' Center for Research in Biological Systems, University of California at San Diego, La Jolla, CA 92093-0043, USA. ' Department of Radiology, University of Arizona, Tucson, AZ 85724, USA

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.

Keywords: SLT; statistical learning theory; radiological imaging; computed tomography; biomarkers; cancer treatment; treatment assessment; radiologist performance; metastatic colorectal carcinoma; patient treatment response; RECIST; WHO measurement methods.

DOI: 10.1504/IJCBDD.2010.034463

International Journal of Computational Biology and Drug Design, 2010 Vol.3 No.1, pp.15 - 18

Published online: 05 Aug 2010 *

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