19687565

Source:http://linkedlifedata.com/resource/pubmed/id/19687565

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0015730, umls-concept:C0024109, umls-concept:C0087111, umls-concept:C1522449, umls-concept:C1704922, umls-concept:C1711411, umls-concept:C1979871
pubmed:issue	18
pubmed:dateCreated	2009-9-2
pubmed:abstractText	We propose a novel approach for potential online treatment verification using cine EPID (electronic portal imaging device) images for hypofractionated lung radiotherapy based on a machine learning algorithm. Hypofractionated radiotherapy requires high precision. It is essential to effectively monitor the target to ensure that the tumor is within the beam aperture. We modeled the treatment verification problem as a two-class classification problem and applied an artificial neural network (ANN) to classify the cine EPID images acquired during the treatment into corresponding classes-with the tumor inside or outside of the beam aperture. Training samples were generated for the ANN using digitally reconstructed radiographs (DRRs) with artificially added shifts in the tumor location-to simulate cine EPID images with different tumor locations. Principal component analysis (PCA) was used to reduce the dimensionality of the training samples and cine EPID images acquired during the treatment. The proposed treatment verification algorithm was tested on five hypofractionated lung patients in a retrospective fashion. On average, our proposed algorithm achieved a 98.0% classification accuracy, a 97.6% recall rate and a 99.7% precision rate.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/0401220
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Sep
pubmed:issn	0031-9155
pubmed:author	pubmed-author:JiangSteveS, pubmed-author:LinTongT, pubmed-author:TangXiaoliX
pubmed:issnType	Print
pubmed:day	21
pubmed:volume	54
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	S1-8
pubmed:meshHeading	pubmed-meshheading:19687565-Aged, pubmed-meshheading:19687565-Artificial Intelligence, pubmed-meshheading:19687565-Dose Fractionation, pubmed-meshheading:19687565-Feasibility Studies, pubmed-meshheading:19687565-Female, pubmed-meshheading:19687565-Humans, pubmed-meshheading:19687565-Lung Neoplasms, pubmed-meshheading:19687565-Male, pubmed-meshheading:19687565-Pattern Recognition, Automated, pubmed-meshheading:19687565-Radiographic Image Interpretation, Computer-Assisted, pubmed-meshheading:19687565-Radiotherapy, Computer-Assisted, pubmed-meshheading:19687565-Radiotherapy, Conformal, pubmed-meshheading:19687565-Reproducibility of Results, pubmed-meshheading:19687565-Sensitivity and Specificity, pubmed-meshheading:19687565-Video Recording, pubmed-meshheading:19687565-X-Ray Intensifying Screens
pubmed:year	2009
pubmed:articleTitle	A feasibility study of treatment verification using EPID cine images for hypofractionated lung radiotherapy.
pubmed:affiliation	Department of Radiation Oncology, University of California San Diego, La Jolla, CA 92093, USA.
pubmed:publicationType	Journal Article, Validation Studies