| pubmed-article:11295347 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:11295347 | lifeskim:mentions | umls-concept:C0024671 | lld:lifeskim |
| pubmed-article:11295347 | lifeskim:mentions | umls-concept:C0242406 | lld:lifeskim |
| pubmed-article:11295347 | lifeskim:mentions | umls-concept:C0008902 | lld:lifeskim |
| pubmed-article:11295347 | lifeskim:mentions | umls-concept:C0205134 | lld:lifeskim |
| pubmed-article:11295347 | pubmed:issue | 1 | lld:pubmed |
| pubmed-article:11295347 | pubmed:dateCreated | 2001-4-11 | lld:pubmed |
| pubmed-article:11295347 | pubmed:abstractText | The aim of this study was to test the performance of artificial neural networks for the classification of signal-time curves obtained from breast masses by dynamic MRI. Signal-time courses from 105 parenchyma, 162 malignant, and 102 benign tissue regions were examined. The latter two groups were histopathologically verified. Four neural networks corresponding to different temporal resolutions of the signal-time curves were tested. The resolution ranges from 28 measurements with a temporal spacing of 23s to just 3 measurements taken 1.8, 3, and 10 minutes after contrast medium administration. Discrimination between malignant and benign lesions is best if 28 measurement points are used (sensitivity: 84%, specificity: 81%). The use of three measurement points results in 78% sensitivity and 76% specificity. These results correspond to values obtained by human experts who visually evaluated signal-time curves without considering additional morphologic information. All examined networks yielded poor results for the subclassification of the benign lesions into fibroadenomas and benign proliferative changes. Neural networks can computationally fast distinguish between malignant and benign lesions even when only a few post-contrast measurements are made. More precise specification of the type of the benign lesion will require incorporation of additional morphological or pharmacokinetic information. | lld:pubmed |
| pubmed-article:11295347 | pubmed:language | eng | lld:pubmed |
| pubmed-article:11295347 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:11295347 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:11295347 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:11295347 | pubmed:month | Jan | lld:pubmed |
| pubmed-article:11295347 | pubmed:issn | 0730-725X | lld:pubmed |
| pubmed-article:11295347 | pubmed:author | pubmed-author:BrinMM | lld:pubmed |
| pubmed-article:11295347 | pubmed:author | pubmed-author:KnoppM VMV | lld:pubmed |
| pubmed-article:11295347 | pubmed:author | pubmed-author:LuchtR ERE | lld:pubmed |
| pubmed-article:11295347 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:11295347 | pubmed:volume | 19 | lld:pubmed |
| pubmed-article:11295347 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:11295347 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:11295347 | pubmed:pagination | 51-7 | lld:pubmed |
| pubmed-article:11295347 | pubmed:dateRevised | 2006-11-15 | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:meshHeading | pubmed-meshheading:11295347... | lld:pubmed |
| pubmed-article:11295347 | pubmed:year | 2001 | lld:pubmed |
| pubmed-article:11295347 | pubmed:articleTitle | Classification of signal-time curves from dynamic MR mammography by neural networks. | lld:pubmed |
| pubmed-article:11295347 | pubmed:affiliation | Division of Medical Radiation Hygiene, Institute of Radiation Hygiene, Federal Office for Radiation Protection, Neuherberg, Germany. | lld:pubmed |
| pubmed-article:11295347 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:11295347 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:11295347 | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:11295347 | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:11295347 | lld:pubmed |
