Linked Life Data

15360765

Source:http://linkedlifedata.com/resource/pubmed/id/15360765

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2004-9-13
pubmed:abstractText
Since the widespread adoption of mammographic screening in the 1980's there has been a significant increase in the detection and biopsy of both benign and malignant microcalcifications. Though current practice standards recommend that the positive predictive value (PPV) of breast biopsy should be in the range of 25-40%, there exists significant variability in practice. Microcalcifications, if malignant, can represent either a non-invasive or an invasive form of breast cancer. The distinction is critical because distinct surgical therapies are indicated. Unfortunately, this information is not always available at the time of surgery due to limited sampling at image-guided biopsy. For these reasons we conducted an experiment to determine whether a previously created Bayesian network for mammography could predict the significance of microcalcifications. In this experiment we aim to test whether the system is able to perform two related tasks in this domain: 1) to predict the likelihood that microcalcifications are malignant and 2) to predict the likelihood that a malignancy is invasive to help guide the choice of appropriate surgical therapy.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0926-9630
pubmed:author
pubmed:issnType
Print
pubmed:volume
107
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
13-7
pubmed:dateRevised
2008-7-10
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography.
pubmed:affiliation
Department of Radiology, University of Wisconsin Medical School, 600 highland Avenue, Madison, WI 53792, USA. bburnside@mail.radiology.wisc.edu
pubmed:publicationType
Journal Article