Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective
Clicks: 377
ID: 109940
2015
Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded tha …
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j2015internationalmachine
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Authors | Kang J;Schwartz R;Flickinger J;Beriwal S;; |
Journal | international journal of radiation oncology, biology, physics |
Year | 2015 |
DOI | DOI not found |
URL | |
Keywords |
support vector machine
artificial intelligence
neural networks
National Center for Biotechnology Information
NCBI
NLM
MEDLINE
review
humans
pubmed abstract
nih
national institutes of health
national library of medicine
pmid:26581149
doi:10.1016/j.ijrobp.2015.07.2286
john kang
russell schwartz
sushil beriwal
esophagitis / etiology
logistic models
lung neoplasms / radiotherapy
machine learning* / statistics & numerical data
machine learning* / trends
models
statistical
computer*
prognosis
radiation oncology*
radiation pneumonitis / etiology
radiotherapy dosage
radiotherapy* / statistics & numerical data
reproducibility of results
treatment outcome
xerostomia / etiology
|
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