OBJECTIVE: To characterize nuclei from well-differentiated, moderately differentiated and poorly differentiated lesions of invasive breast cancer by karyometry and to test the hypothesis that these diagnostic categories form homogeneous sets. STUDY DESIGN: Histopathologic sections from 6 cases of well-differentiated, 11 cases of moderately differentiated and 17 cases of poorly differentiated ductal carcinomas were digitally recorded. From each case 100 nuclei were segmented and analyzed by karyometry. A discriminant analysis was performed, and nuclear and lesion signatures were computed. The nonsupervised learning algorithm P-index was applied. A progression curve per diagnostic category based on mean nuclear abnormality and a discriminant function score was derived. RESULTS: The well-differentiated lesions formed a homogeneous set, but both the moderately and poorly differentiated lesions showed 2 significantly different subpopulations with nuclei of substantially different nuclear abnormality and progression. CONCLUSION: The visual histopathologic diagnostic assessment of these lesions was based on an evaluation of both tissue architectural criteria and nuclear criteria. Here, only the pattern of nuclear chromatin was evaluated. Cases belonging to the same diagnostic category as assessed by their differentiation may be further characterized by the extent to which the nuclei deviate from normal. There was substantial case-to-case heterogeneity in these invasive lesions.
OBJECTIVE: To characterize nuclei from well-differentiated, moderately differentiated and poorly differentiated lesions of invasive breast cancer by karyometry and to test the hypothesis that these diagnostic categories form homogeneous sets. STUDY DESIGN: Histopathologic sections from 6 cases of well-differentiated, 11 cases of moderately differentiated and 17 cases of poorly differentiated ductal carcinomas were digitally recorded. From each case 100 nuclei were segmented and analyzed by karyometry. A discriminant analysis was performed, and nuclear and lesion signatures were computed. The nonsupervised learning algorithm P-index was applied. A progression curve per diagnostic category based on mean nuclear abnormality and a discriminant function score was derived. RESULTS: The well-differentiated lesions formed a homogeneous set, but both the moderately and poorly differentiated lesions showed 2 significantly different subpopulations with nuclei of substantially different nuclear abnormality and progression. CONCLUSION: The visual histopathologic diagnostic assessment of these lesions was based on an evaluation of both tissue architectural criteria and nuclear criteria. Here, only the pattern of nuclear chromatin was evaluated. Cases belonging to the same diagnostic category as assessed by their differentiation may be further characterized by the extent to which the nuclei deviate from normal. There was substantial case-to-case heterogeneity in these invasive lesions.
Authors: Naomi A Miller; Judith-Anne W Chapman; Jin Qian; William A Christens-Barry; Yuejiao Fu; Yan Yuan; H Lavina A Lickley; David E Axelrod Journal: Cancer Inform Date: 2010-09-07