To characterize nuclei from breast solid pattern ductal carcinoma in situ (DCIS) by their karyometric features and to search for the presence of statistically significantly different subsets of nuclei.
One hundred nuclei from each of 6 normal, 13 solid DCIS, (9 low and intermediate grade and 4 high grade DCIS) histopathologic samples of breast tissue were digitally recorded. Karyometric features were computed and subjected to a nonsupervised learning algorithm (P-index) to identify significantly different subgroups.
Nuclei in low grade lesions displayed a diploid/near diploid pattern, while the majority of intermediate grade lesions fell into a range beyond 5N. The high grade lesions showed substantial genomic instability and represented three statistically different subsets or phenotypes.
There is a progression of nuclear abnormality from low grade to high grade DCIS. The nuclei from high grade DCIS form a heterogeneous set that represents three phenotypes. One of these phenotypes shows a nuclear chromatin pattern that more closely resembles poorly differentiated, infiltrating disease. The observation of such a phenotype may have prognostic implications.
"Computer-aided image analysis is such a method. Image analysis has been used to extract quantitative nuclear information useful for diagnosis of biopsy specimens of many tissues, including breast DCIS, and invasive breast (Kerlikowske et al. 2003; Carpenter et al. 1985; Dey et al. 2000; Frank et al. 2001; Hoque et al. 2001; Mariuzzi et al. 1996; Mommers et al. 2001; Susnik et al. 1995; Tuczek et al. 1996; Wolberg et al. 1995). However, these previous reports did not focus on evaluating information for individual nuclei in the classifi cation of patients with mixed nuclear grades. "
[Show abstract][Hide abstract] ABSTRACT: Nuclear grade has been associated with breast DCIS recurrence and progression to invasive carcinoma; however, our previous study of a cohort of patients with breast DCIS did not find such an association with outcome. Fifty percent of patients had heterogeneous DCIS with more than one nuclear grade. The aim of the current study was to investigate the effect of quantitative nuclear features assessed with digital image analysis on ipsilateral DCIS recurrence.
Hematoxylin and eosin stained slides for a cohort of 80 patients with primary breast DCIS were reviewed and two fields with representative grade (or grades) were identified by a Pathologist and simultaneously used for acquisition of digital images for each field. Van Nuys worst nuclear grade was assigned, as was predominant grade, and heterogeneous grading when present. Patients were grouped by heterogeneity of their nuclear grade: Group A: nuclear grade 1 only, nuclear grades 1 and 2, or nuclear grade 2 only (32 patients), Group B: nuclear grades 1, 2 and 3, or nuclear grades 2 and 3 (31 patients), Group 3: nuclear grade 3 only (17 patients). Nuclear fine structure was assessed by software which captured thirty-nine nuclear feature values describing nuclear morphometry, densitometry, and texture. Step-wise forward Cox regressions were performed with previous clinical and pathologic factors, and the new image analysis features.
Duplicate measurements were similar for 89.7% to 97.4% of assessed image features. The rate of correct classification of nuclear grading with digital image analysis features was similar in the two fields, and pooled assessment across both fields. In the pooled assessment, a discriminant function with one nuclear morphometric and one texture feature was significantly (p = 0.001) associated with nuclear grading, and provided correct jackknifed classification of a patient's nuclear grade for Group A (78.1%), Group B (48.4%), and Group C (70.6%). The factors significantly associated with DCIS recurrence were those previously found, type of initial presentation (p = 0.03) and amount of parenchymal involvement (p = 0.05), along with the morphometry image feature of ellipticity (p = 0.04).
Analysis of nuclear features measured by image cytometry may contribute to the classification and prognosis of breast DCIS patients with more than one nuclear grade.
[Show abstract][Hide abstract] ABSTRACT: The chromatin pattern in nuclei from breast ductal proliferative lesions was quantitatively evaluated with the objective of deriving measures of tumor progression. A total of 110 cases were analyzed. There were 38 cases of normal tissue or benign proliferative lesions, 41 cases of ductal carcinoma in situ (DCIS), and 31 cases of microinfiltrating DCIS and of infiltrating cancer. A total of 9424 nuclei were analyzed. High-resolution images were digitally recorded. For each nucleus, 93 karyometric features descriptive of the spatial and statistical distribution of the nuclear chromatin were computed. Data analysis included establishing a profile of relative deviations of each feature from "normal," called the nuclear signature, and of lesion signatures as well as of trends of lesion progression. Two trends of evolution could be discerned: one from normal to hyperplasia, atypical hyperplasia, and comedo DCIS as representative of high-grade lesions; and the other from normal to hyperplasia to cribriform DCIS, solid DCIS, and infiltrating cancer, representing lower grade lesions. The nuclei in microinfiltrating foci are distinctly different from nuclei in high-grade comedo DCIS. The nuclei in microinfiltrating foci have a statistically significantly lower nuclear abnormality. They may represent outgrowing clones.
Modern Pathology 02/2002; 15(1):18-25. DOI:10.1038/modpathol.3880485 · 6.19 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In digital pathology, the field of nuclear texture analysis gives information about the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image, providing statistical texture measures that may be used as quantitative tools for diagnosis and prognosis of human cancer. In the present work, we have reviewed nuclear texture analysis in human cancer research, with emphasis on (i) statistical texture analysis methods, (ii) methods for feature evaluation and feature set selection, (iii) classification methods and error estimation, and (iv) the recent literature in the field, focusing on diagnosis- and prognosis-related applications. The application study covers the period from 1995 to 2007. In order to find nuclear features that discriminate robustly between cases from different diagnostic or prognostic classes, a statistical evaluation of features must be performed, and this demands careful experimental design. The present review reveals that it is quite common to evaluate a large number of features on a limited learning set of clinical material, without testing the chosen classifier on an independent validation data set. This easily leads to overoptimistic results. Out of 160 papers, we found only 30 papers in which the classifier was evaluated on an independent validation data set. Even in these studies, some good results have been hampered by small validation groups. However, it is encouraging to note that those publications meeting the requirements of an optimal study are generally showing good results. Thus, it is well documented that nuclear texture analysis is showing promising results as a novel diagnostic and/or prognostic marker. Hopefully, we will soon see that these promising studies will be replicated in large, prospective, multicenter trials.
Critical reviews in oncogenesis 02/2008; 14(2-3):89-164. DOI:10.1615/CritRevOncog.v14.i2-3.10
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