ArticlePloS one2013
Development of a nuclear morphometric signature for prostate cancer risk in negative biopsies.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Extracellular Matrix Components and Mechanosensing Pathways in Health and Disease.Biomolecules · 2024Review
- Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas.Cancers · 2021Article
- Modelling Nuclear Morphology and Shape Transformation: A Review.Membranes · 2021Review
- Diagnosing 12 prostate needle cores within an hour of biopsy via open-top light-sheet microscopy.Journal of biomedical optics · 2020Article
- Multiple particle tracking analysis in isolated nuclei reveals the mechanical phenotype of leukemia cells.Scientific reports · 2020Article
- Advances in the computational and molecular understanding of the prostate cancer cell nucleus.Journal of cellular biochemistry · 2018Review
- Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis.Scientific reports · 2017Article
- Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings.European urology focus · 2017Article
- Epithelial-mesenchymal transition in prostate cancer is associated with quantifiable changes in nuclear structure.The Prostate · 2015Article
- Evolution: Back to the future to understand and control prostate cancer.Asian journal of urology · 2014Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundOur objective was to develop and validate a multi-feature nuclear score based on image analysis of direct DNA staining, and to test its association with field effects and subsequent detection of prostate cancer (PCa) in benign biopsies.
methodsTissue sections from 39 prostatectomies were Feulgen-stained and digitally scanned (400×), providing maps of DNA content per pixel. PCa and benign epithelial nuclei were randomly selected for measurement of 52 basic morphometric features. Logistic regression models discriminating benign from PCa nuclei, and benign from malignant nuclear populations, were built and cross-validated by AUC analysis. Nuclear populations were randomly collected <1 mm or >5 mm from cancer foci, and from cancer-free prostates, HGPIN, and PCa Gleason grade 3-5. Nuclei also were collected from negative biopsy subjects who had a subsequent diagnosis of PCa and age-matched cancer-free controls (20 pairs).
resultsA multi-feature nuclear score discriminated cancer from benign cell populations with AUCs of 0.91 and 0.79, respectively, in training and validation sets of patients. In prostatectomy samples, both nuclear- and population-level models revealed cancer-like features in benign nuclei adjacent to PCa, compared to nuclei that were more distant or from PCa-free glands. In negative biopsies, a validated model with 5 variance features yielded significantly higher scores in cases than controls (P = 0.026).
conclusionsA multifeature nuclear morphometric score, obtained by automated digital analysis, was validated for discrimination of benign from cancer nuclei. This score demonstrated field effects in benign epithelial nuclei at varying distance from PCa lesions, and was associated with subsequent PCa detection in negative biopsies. IMPACT: This nuclear score shows promise as a risk predictor among men with negative biopsies and as an intermediate biomarker in Phase II chemoprevention trials. The results also suggest that subvisual disturbances in nuclear structure precede the development of pre-neoplastic lesions.
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