Cancer Research AACR Membership  Sign up for Cancer Research eTOC's
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Cancer Research Clinical Cancer Research
Cancer Epidemiology Biomarkers & Prevention Molecular Cancer Therapeutics
Molecular Cancer Research Cancer Prevention Research
Cancer Prevention Journals Portal Cancer Reviews Online
Annual Meeting Education Book Meeting Abstracts Online

[Cancer Research 44, 178-183, January 1, 1984]
© 1984 American Association for Cancer Research

This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Nychka, D.
Right arrow Articles by Goldfarb, S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Nychka, D.
Right arrow Articles by Goldfarb, S.

Optimal Use of Sampled Tissue Sections for Estimating the Number of Hepatocellular Foci1

Douglas Nychka2, Thomas D. Pugh, James H. King3, Hirofumi Koen4, Grace Wahba, Joshua Chover and Stanley Goldfarb5

Departments of Statistics [D. N., G. W.], Pathology [T. D. P., J. H. K., H. K., S. G.], and Mathematics [J. C.], University of Wisconsin, Madison, Wisconsin 53706

Statistical techniques were applied to computer-simulated data to evaluate the importance of various factors that may affect the estimation of the number of hepatocellular foci from foci transections. The simulations were modeled after mouse foci for which three-dimensional size distributions and densities were determined from serial section reconstructions. The foci had been induced in male C57BL/6 x C3H F1 mice at 20 and 28 weeks after a single injection, in infancy, of diethylnitrosamine. In an earlier report, we had shown that the number of foci per cu cm could be accurately estimated from profiles using a conditional estimator when the investigator is unable to identify profiles smaller than a certain size ({varepsilon}). In the present study, emphasis was placed on assessing the value of step serial sections in order to make optimal use of the small tissue samples in mouse liver.

Since all mathematical estimators of N3 are based on measurement of sampled profiles (n2{varepsilon}) and ultimately derive from the fundamental relationship

Formula
(where N3{varepsilon} is the number of foci per cu cm, N2{varepsilon} is the number of profiles per sq cm, and µ{varepsilon} is the average adjusted focus radius), the relative importance of N2{varepsilon} and µ{varepsilon} on the conditional estimator was evaluated. This was accomplished by comparing the errors resulting from use of the conditional estimator with those resulting from use of two other estimators. The latter two estimators consisted of a "sampled focus estimator," which used sampled intact foci to estimate N3, and a "reference estimator," which used profiles from foci with a µ{varepsilon} that was known. Additionally, in order to provide a stable variance for the conditional estimator, we adopted a simple smoothing procedure. As expected, none of the estimators showed any significant bias. However, somewhat surprising was the finding that the standard deviations from use of all three estimators were almost identical. Consequently, it appears that the variance resulting from application of the smoothed conditional estimator to large sets of profile data is not due to difficulty in estimating µ{varepsilon}. In these instances, the faulty estimates of N2{varepsilon} resulted almost entirely from the variability in sampling foci from tissue blocks which constituted only about 1/25 of the liver volume.

In addition, the ability to reduce the error in estimating the number of foci by increasing the number of profiles was also evaluated. We simulated a sectioning protocol for the 28-week mouse livers in which the distances between the 5-µm-thick step sections in a 1-mm block of liver were progressively decreased from 1000 to 50 µm. For the particular size distribution and density of simulated foci, the error in estimating the number of foci per cu cm from profiles in 11-step sections was only slightly greater than that obtained by estimating the numerical density of foci from the actual three-dimensional sample. We conclude that the application of an appropriate estimator to profiles of foci in step sections should be of great value for estimating the number of spheroidal neoplastic and preneoplastic foci. Furthermore, computer simulations should enable the investigator to determine the advantages and limitations of particular sectioning schemes prior to embarking on time-consuming projects.

1 Supported by NIH Grant CA15664, by a contract from the University of Chicago under NIH Grant CA25522, and by ARO Grant DAAG29-80-K-0042. This is the second in a series of reports on the use of mathematical-stereological methods for studying microscopic foci (21).

2 Present address: Department of Statistics, North Carolina State University, Raleigh, N. C. 27607.

3 Recipient of a fellowship award from NIH Training Grant 5-T32-ES 07015-07. Present address: Department of Industrial Engineering, Northwestern University, Evanston, Ill. 60201.

4 Present address: The First Department of Medicine, Chiba University School of Medicine, Chiba City (280), Japan.

5 To whom requests for reprints should be addressed.

Received 11/ 1/82. Accepted 10/ 7/83.




This article has been cited by other articles:


Home page
Toxicol PatholHome page
T. L. Goldsworthy and R. Fransson-Steen
Quantitation of the Cancer Process in C57BL/6J, B6C3F1 and C3H/HeJ Mice
Toxicol Pathol, January 1, 2002; 30(1): 97 - 105.
[Abstract] [PDF]




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Cancer Research Clinical Cancer Research
Cancer Epidemiology Biomarkers & Prevention Molecular Cancer Therapeutics
Molecular Cancer Research Cancer Prevention Research
Cancer Prevention Journals Portal Cancer Reviews Online
Annual Meeting Education Book Meeting Abstracts Online
Copyright © 1984 by the American Association for Cancer Research.