This article is part of the supplement: The ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS)

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

Walker H Land1*, Dan Margolis1, Ronald Gottlieb2, Elizabeth A Krupinski2 and Jack Y Yang34

Author Affiliations

1 Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA

2 Department of Radiology, University of Arizona, Tucson, AZ 85724, USA

3 Center for Research in Biological Systems, University of California at San Diego, La Jolla, California 92093-0043 USA

4 Department of Radiation Oncology, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, Massachusetts 02114 USA

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BMC Genomics 2010, 11(Suppl 3):S15  doi:10.1186/1471-2164-11-S3-S15

Published: 1 December 2010



Significant interest exists in establishing radiologic imaging as a valid biomarker for assessing the response of cancer to a variety of treatments. To address this problem, we have chosen to study patients with metastatic colorectal carcinoma to learn whether statistical learning theory can improve the performance of radiologists using CT in predicting patient treatment response to therapy compared with the more traditional RECIST (Response Evaluation Criteria in Solid Tumors) standard.


Predictions of survival after 8 months in 38 patients with metastatic colorectal carcinoma using the Support Vector Machine (SVM) technique improved 30% when using additional information compared to WHO (World Health Organization) or RECIST measurements alone. With both Logistic Regression (LR) and SVM, there was no significant difference in performance between WHO and RECIST. The SVM and LR techniques also demonstrated that one radiologist consistently outperformed another.


This preliminary research study has demonstrated that SLT algorithms, properly used in a clinical setting, have the potential to address questions and criticisms associated with both RECIST and WHO scoring methods. We also propose that tumor heterogeneity, shape, etc. obtained from CT and/or MRI scans be added to the SLT feature vector for processing.