Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning.
Int J Comput Biol Drug Des
; 3(1): 15-8, 2010.
Article
em En
| MEDLINE
| ID: mdl-20693607
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.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Neoplasias Colorretais
/
Tomografia Computadorizada por Raios X
/
Modelos Estatísticos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Int J Comput Biol Drug Des
Assunto da revista:
BIOLOGIA
/
FARMACOLOGIA
Ano de publicação:
2010
Tipo de documento:
Article
País de afiliação:
Estados Unidos