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A Grading Scheme for the Assessment of Proliferative Lesions of the Mouse Prostate in the TRAMP ModelIntegrated Laboratory Systems, Research Triangle Park, North Carolina 27709, asuttie{at}ils-inc.com
Laboratory of Experimental Pathology
Biostatistics Branch, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
Integrated Laboratory Systems, Research Triangle Park, North Carolina 27709
Integrated Laboratory Systems, Research Triangle Park, North Carolina 27709
Integrated Laboratory Systems, Research Triangle Park, North Carolina 27709 To improve the precision and consistency of experimental results, we have developed a scoring system for proliferative epithelial lesions in the mouse prostate based on histological growth patterns observed in individual lobes. Severity of proliferative lesions was divided into 6 categories; the grade of the most advanced lesion was identified for each lobe and its distribution estimated semiquantitatively. A numerical score combining grade and distribution of the most advanced lesion in each lobe was assigned and termed the "distribution-adjusted lesion grade"; the mean of these scores was calculated for each treatment group. Using this grading scheme, we assessed lesion development in ad libitum-fed and 20%-diet-restricted groups of TRAMP (Transgenic Adenocarcinoma of Mouse Prostate) mice that were started on study at 7 weeks of age and sacrificed when 11 and 20 weeks old. The anterior, dorsal, lateral, and ventral prostate lobes showed clear reductions in lesion severity in diet-restricted TRAMPs at 11 and 20 weeks. This method for scoring the epithelial pathology of the prostate in the TRAMP model with minimal to severe proliferative lesions utilizes the natural history of lesion development for assessing the effects of chemical and dietary interventions.
Key Words: Grading quantitation histopathology mouse prostatic cancer TRAMP.
Toxicologic Pathology, Vol. 31, No. 1,
31-38 (2003) This article has been cited by other articles:
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