Year: 1991
Type: article
Study design: Observational Study
Abstract: Alterations in nuclear structure are the morphologic hallmark of cancer diagnosis. Nuclear size, shape, chromatin pattern, and nucleolar size and number have all been reported to change in breast cancer. Attempts to quantify nuclear alterations to establish grading systems, predict prognosis, and/or set guidelines for therapy have met with varied success. Therefore, the authors quantified the changes that occur with breast cancer with nuclear morphology in several different groups of patients: normal controls, intraductal carcinoma, invasive ductal carcinoma with negative nodes at mastectomy, and invasive ductal carcinoma with positive lymph nodes. Pleomorphism as measured by both nuclear area and intrasample variation increased with invasive histology and metastatic breast cancer. It is still unclear whether node-negative Stage II breast cancer requires adjuvant therapy. This issue would be less complicated if it were possible to identify those women at high risk of recurrence. Therefore, the authors retrospectively identified 30 women with node-negative Stage II infiltrating ductal carcinoma with a long follow-up period of 6 to 12 years. Computer-assisted morphometry of nuclei in routine hematoxylin and eosin-stained pathologic slides was done using the DynaCell Analysis System in a blinded fashion. DynaCell measures 15 nuclear parameters, including perimeter, area volume, roundness, and ellipticity. Although nuclear area and variance were related to breast cancer progression, nuclear morphometry did not predict successfully which patients would have recurrent disease in the women with Stage II, node-negative lesions at the time of mastectomy.
Source: Cancer
Authors: Kenneth J. Pienta, Donald S. Coffey
Institutions: Johns Hopkins University, Johns Hopkins Medicine
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Cancer Institute
Award: G841297785
OpenAlex ID: W2085308158