Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning.
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.
Land
Walker H
WH
Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA. dmatgo11@binghamton.edu
Margolis
Dan
D
Gottlieb
Ronald
R
Yang
Jack Y
JY
Krupinski
Elizabeth A
EA
D000465:Algorithms; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000628:therapy; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D009362:Neoplasm Metastasis; D011237:Predictive Value of Tests; D014057:Tomography, X-Ray Computed / Q000379:methods*; D016896:Treatment Outcome