ArticleComputational and structural biotechnology journal2020
Derivation of a nuclear heterogeneity image index to grade DCIS.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Scalable and Efficient Deep Learning-Based Pipeline for Mitotic Detection and Analysis in Pathology Images.Cancers · 2026Article
- Integrating Multiple Methods to Validate Key Genes Driving the Progression of Breast Ductal Carcinoma In Situ.Current issues in molecular biology · 2025Article
- Laser speckle particle sizer (SPARSE) informs the size distribution of tissue granularities.Science advances · 2025Article
- Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance.Signal transduction and targeted therapy · 2024Review
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- Review
- Improving DCIS diagnosis and predictive outcome by applying artificial intelligence.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- Tissue mechanics in stem cell fate, development, and cancer.Developmental cell · 2021Review
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
Abnormalities in cell nuclear morphology are a hallmark of cancer. Histological assessment of cell nuclear morphology is frequently used by pathologists to grade ductal carcinoma in situ (DCIS). Objective methods that allow standardization and reproducibility of cell nuclear morphology assessment have potential to improve the criteria needed to predict DCIS progression and recurrence. Aggressive cancers are highly heterogeneous. We asked whether cell nuclear morphology heterogeneity could be incorporated into a metric to classify DCIS. We developed a nuclear heterogeneity image index to objectively, and quantitatively grade DCIS. A whole-tissue cell nuclear morphological analysis, that classified tumors by the worst ten percent in a duct-by-duct manner, identified nuclear size ranges associated with each DCIS grade. Digital image analysis further revealed increasing heterogeneity within ducts or between ducts in tissues of worsening DCIS grade. The findings illustrate how digital image analysis comprises a supplemental tool for pathologists to objectively classify DCIS and in the future, may provide a method to predict patient outcome through analysis of nuclear heterogeneity.
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