Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning
by Walker H. Land Jr., Dan Margolis, Ronald Gottlieb, Jack Y. Yang, Elizabeth A. Krupinski
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 3, No. 1, 2010

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.

Online publication date: Thu, 05-Aug-2010

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