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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1993-3-24
pubmed:abstractText
Prostate cancer is currently the most commonly diagnosed cancer among males in the United States. As technology improves and the search for this enigmatic condition intensifies, we are detecting greater numbers of non-palpable tumours. These tumours are generally treated aggressively, given the uncertainty of their behaviour, but this approach may be over-zealous for small volume disease. The likelihood of detecting any cancer volume can be derived from Bayes' theorem of conditional probability. A laboratory model using coloured clay was created to contrast tumour volumes of 2.5, 5 and 20% (n = 75). Six random systematic biopsies were taken from each model in a blind fashion; 36% of the 2.5%, 44% of the 5% and all of the 20% models had at least 1 positive biopsy. Twenty-two of the 25 models representing 20% tumour had 3 or more biopsy cores positive. These data suggest that low volume disease with low biological potential will be found by random biopsy as the mathematical probability predicts. The high incidence of occult prostate cancer in the older population makes this a worrying observation. Also, and perhaps more important, there is a direct correlation between the volume of disease and the number of positive biopsies. This correlation is easily seen in both models and may allow for an estimation of tumour volume. This ability to estimate tumour volume may be a useful clinical tool that helps to guide therapy and assess prognosis.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0007-1331
pubmed:author
pubmed:issnType
Print
pubmed:volume
71
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
43-6
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
1993
pubmed:articleTitle
Detection of non-palpable prostate cancer. A mathematical and laboratory model.
pubmed:affiliation
Section of Urology, University of Michigan Medical Center, Ann Arbor.
pubmed:publicationType
Journal Article