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

Land, Walker H; Margolis, Dan; Gottlieb, Ronald; Krupinski, Elizabeth A; Yang, Jack Y

Land, Walker H; Margolis, Dan; Gottlieb, Ronald; Krupinski, Elizabeth A; Yang, Jack Y.

Affiliation

Land WH; Department of Bioengineering, Binghamton University, Binghamton, NY 13903-6000, USA. wland@binghamton.edu

BMC Genomics ; 11 Suppl 3: S15, 2010 Dec 01.

Article in En | MEDLINE | ID: mdl-21143782

Subject(s)

Carcinoma/diagnostic imaging; Carcinoma/secondary; Colorectal Neoplasms/diagnostic imaging; Tomography, X-Ray Computed; Area Under Curve; Biomarkers, Tumor; Carcinoma/drug therapy; Carcinoma/mortality; Colorectal Neoplasms/drug therapy; Colorectal Neoplasms/mortality; Colorectal Neoplasms/pathology; Humans; Logistic Models; Odds Ratio; ROC Curve; Software; Survival Analysis

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Carcinoma / Colorectal Neoplasms / Tomography, X-Ray Computed Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: BMC Genomics Journal subject: GENETICA Year: 2010 Document type: Article Affiliation country: United States Country of publication: United kingdom

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