Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning. (6th August 2010)

• Record Type: Journal Article
• Authors: Land Jr., Walker H.; Margolis, Dan; Gottlieb, Ronald; Yang, Jack Y.; , Elizabeth A. Krupinski
• Is Part Of: International journal of computational biology and drug design. Volume 3:Number 1(2010)
• Page Start: 15
• Page End: 18
• Publication Date: 2010-08-06
• Subjects: 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; Computational biology -- Periodicals; Drugs -- Design -- Periodicals; 570.285
• Languages: English
• ISSNs: 1756-0756

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.