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J Urol. 1998 Apr;159(4):1260-4.

Computer modeling of prostate biopsy: tumor size and location--not clinical significance--determine cancer detection.

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1
Department of Pathology, University of Colorado Health Sciences Center, Denver, USA.

Abstract

PURPOSE:

Sampling error is an inherent problem of prostate biopsy, and the determination of clinical significance based on biopsy results is problematic. We quantify the dimensions of these problems by computer simulation.

MATERIALS AND METHODS:

We constructed 3-dimensional solid computer models of 59 autopsy prostates containing clinically undetected prostate cancer, and performed simulations of the standard prostate biopsy method.

RESULTS:

Biopsy simulation detected 19 tumors from the 59 prostates, the majority of which were in the most accessible portion of the prostate, the posterior peripheral zone. Using 0.5 cc or greater tumor volume or less than 0.5 cc and Gleason sum 7 or greater as criteria of significance, the model detected 58% (11 of 19) significant tumors and 20% (8 of 40) insignificant tumors. With 0.25 cc or greater tumor volume or less than 0.25 cc and Gleason sum 7 or greater as criteria 15 of 29 significant (52%) and 4 of 30 insignificant (13%) tumors were detected. Among significant tumors defined by either volume criterion there was a statistical difference between detected and undetected tumors in terms of mean tumor volume and mean ratio of tumor volume-to-prostate volume. Among insignificant tumors defined by either criterion there was no such difference.

CONCLUSIONS:

As much as 20 to 40% of currently detected prostate cancer may be histologically insignificant, as 4 of 19 cancers were detected when 0.25 cc was used as volume determinant of clinical significance and 8 of 19 were detected when 0.5 cc volume was used. These tumors are detected randomly. On the other hand, perhaps only one-half to three-fourths of clinically significant prostate cancers are being detected, and then only because the volume and anatomic location make them hard to miss.

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PMID:
9507848
[Indexed for MEDLINE]
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