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Cellular Oncology
Volume 26 (2004), Issue 1-2, Pages 31-43
http://dx.doi.org/10.1155/2004/238769

Quantitative Histopathological Analysis of Cervical Intra-Epithelial Neoplasia Sections: Methodological Issues

Martial Guillaud,1 Dennis Cox,2 Anais Malpica,3 Gregg Staerkel,3 Jasenka Matisic,4 Dirk Van Niekirk,4 Karen Adler‐Storthz,5 Neal Poulin,1 Michele Follen,6,7 and Calum MacAulay1

1Dept. of Cancer Imaging, BC Cancer Agency, Vancouver, BC, Canada
2Dept. of Statistics, Rice University, Houston, TX, USA
3Dept. of Pathology, UT M.D. Anderson Cancer Center, Houston, TX, USA
4Dept. of Pathology, BC Cancer Agency, Vancouver, BC, Canada
5Dept. of Research Dentistry, University of Texas Health Science Center at Houston, Houston, TX, USA
6Dept. of Gynecologic Oncology, Center for Biomedical Engineering, UT M.D. Anderson Cancer Center, Houston, TX, USA
7Dept. of Obstetrics, Gynecology and Reproductive Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA

Received 31 March 2003; Accepted 21 October 2003

Copyright © 2004 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives: As part a Program Project to evaluate emerging optical technologies for cervical neoplasia, our group is performing quantitative histopathological analysis of biopsies from 1800 patients. Several methodological issues have arisen with respect to this analysis: (1) Finding the most efficient way to compensate for staining intensity variation with out losing diagnostic information; (2) Assessing the inter‐ and intra‐observer variability of the semi‐interactive data collection; and (3) the use of non‐overlapping cells from the intermediate layer only. Methods: Non‐overlapping quantitatively stained nuclei were selected from 280 samples with histopathological characteristics of normal (199), koilocytosis (37), CIN 1 (18), CIN 2 (10) and CIN 3 (16). Linear discriminant analysis was used to assess the diagnostic information in three different feature sets to evaluate and compare staining intensity normalization methods. Selected feature values and summary scores were used to evaluate intra‐ and inter‐observer variability. Results: The features normalized by the internal subset of the imaged cells had the same discriminatory power as those normalized by the control cells and by both normalization methods seem to have additional discriminatory power over the set of features which do not require normalization. The use of the internal subset decreased the image acquisition time by ∼50% at each center, respectively. The intra‐ and inter‐observer variability was of a similar size. Good performance was obtained by measuring the intermediate layer only. Conclusion: The use of intensity normalization from a subset of the imaged non‐overlapping intermediate layer cells works as well as or better than any of the other methods tested and provides a significant timesaving. Our intra‐ and inter‐observer variability do not seem to affect the diagnostic power of the data. Although this must be tested in a larger data set, the use of intermediate layer cells only may be acceptable when using quantitative histopathology.