1Department of Genetics and Cancer Institute of New Jersey, Rutgers—The State University of New Jersey, 604 Allison Road, Piscataway, NJ 08854-8082, U.S.A. 2Department of Pathology, University Health Network and University of Toronto, 610 University Avenue, Toronto, Ontario, Canada. M5G 2M9. 3Henrietta Banting Breast Cancer Centre, Women’s College Hospital, University of Toronto, 76 Grenville Street, 7th floor, Toronto, Ontario, Canada. M5S 1B2. 4Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada. N2L 3G1. 5Equipoise Imaging LLC , 4009 St. Johns Lane, Ellicott City, MD 21042, U.S.A. 6Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, Ontario, Canada. M3J 1P3. 7National Cancer Institute of Canada Clinical Trials Group, Queen’s University, 10 Stuart Street, Kingston, Ontario, Canada. K7L 3N6.
Background: 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.
Methods: 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 fi ne 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.
Results: 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).
Conclusion: 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.
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