Datamining approaches for modeling tumor control probability.
about
A data-mining framework for large scale analysis of dose-outcome relationships in a database of irradiated head and neck cancer patients.Standardized data collection to build prediction models in oncology: a prototype for rectal cancer.A Bayesian network approach for modeling local failure in lung cancer.Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning.Radiogenomics and radiotherapy response modeling.Biological function integrated prediction of severe radiographic progression in rheumatoid arthritis: a nested case control study.The future of predictive models in radiation oncology: from extensive data mining to reliable modeling of the results.Motivation for the inclusion of genetic risk factors of radiosensitivity alongside dosimetric and clinical parameters in predicting normal tissue effects.Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs.
P2860
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P2860
Datamining approaches for modeling tumor control probability.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
2010年论文
@zh
2010年论文
@zh-cn
name
Datamining approaches for modeling tumor control probability.
@ast
Datamining approaches for modeling tumor control probability.
@en
type
label
Datamining approaches for modeling tumor control probability.
@ast
Datamining approaches for modeling tumor control probability.
@en
prefLabel
Datamining approaches for modeling tumor control probability.
@ast
Datamining approaches for modeling tumor control probability.
@en
P2093
P2860
P1433
P1476
Datamining approaches for modeling tumor control probability.
@en
P2093
Aditya Apte
Andrew J Hope
Ellen Huang
Issam El Naqa
James Alaly
Jeffrey D Bradley
Patricia E Lindsay
P2860
P304
P356
10.3109/02841861003649224
P50
P577
2010-03-02T00:00:00Z