Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Nuclear chromatin characteristics of breast solid pattern ductal carcinoma in situ.

Analytical and quantitative cytology and histology - 1 Dec 2001

Frank D H, Davis J R, Alberts D S, Thompson D, Liu Y, Bartels P H

Abstract excerpt

OBJECTIVE: 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. STUDY DESIGN: 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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Breast Neoplasms
Share to Carcinoma in Situ
Share to Carcinoma, Intraductal, Noninfiltrating

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Chromatin

Share to Image Processing, Computer-Assisted

Share to Phenotype

Share to Ploidies

Source

Identifiers

PMID: 11777277

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Nuclear chromatin characteristics of breast solid pattern ductal carcinoma in situ.PMID 11777277Read ↗

Karyometry of nuclear phenotypes in cutaneous squamous cell cancer.Semantic proximity Read ↗

AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosisSemantic proximity Read ↗

Deep-learning quantified cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer typesSemantic proximity Read ↗

Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and CharacterisationSemantic proximity Read ↗

Single cell imaging-based chromatin biomarkers for tumor progression.Semantic proximity Read ↗

High Proportion of Nuclear Phenotype Identifies Aggressive Cutaneous Squamous Cell Carcinoma.Semantic proximity Read ↗

Karyometry in atypical endometrial hyperplasia: a Gynecologic Oncology Group study.Semantic proximity Read ↗

Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis.Semantic proximity Read ↗

Molecular Grading of Ductal Carcinoma In situ of the BreastSemantic proximity Read ↗

Nuclear Morphology Optimized Deep Hybrid Learning (NUMODRIL): A novel architecture for accurate diagnosis/prognosis of Ovarian CancerSemantic proximity Read ↗

The prognostic significance of determining DNA content in breast cancer by DNA image cytometry: the role of high grade aneuploidy in node negative breast cancerSemantic proximity Read ↗

Nuclear Morphology Optimized Deep Hybrid Learning (NUMODRIL) For Accurate Diagnosis and Prognosis of Ovarian CancerSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.