ArticleJNCI cancer spectrum2021
Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer.
Article in JNCI cancer spectrum, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 30 citations in OpenAlex.
- Air Pollutants and Breast Cancer Risk: A Parallel Analysis of Five Large US Prospective Cohorts.American journal of public health · 2025Article
- Spatially Resolved Single-Cell Morphometry of Benign Breast Disease Biopsy Images Uncovers Quantitative Cytomorphometric Features Predictive of Subsequent Invasive Breast Cancer Risk.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Deep learning analysis of hematoxylin and eosin-stained benign breast biopsies to predict future invasive breast cancer.JNCI cancer spectrum · 2025Article
- Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals.Breast cancer research : BCR · 2024Article
- Benign Breast Disease and Breast Cancer Risk in the Percutaneous Biopsy Era.JAMA surgery · 2024Article
- Host, reproductive, and lifestyle factors in relation to quantitative histologic metrics of the normal breast.Breast cancer research : BCR · 2023Article
- Outdoor air pollution and histologic composition of normal breast tissue.Environment international · 2023Article
- Associations of alcohol consumption with breast tissue composition.Breast cancer research : BCR · 2023Article
- Drug-disease association prediction with literature based multi-feature fusion.Frontiers in pharmacology · 2023Article
- Histological Findings of Mammary Gland Development and Risk of Breast Cancer inJournal of breast cancer · 2021Article
- Associations of reproductive breast cancer risk factors with breast tissue composition.Breast cancer research : BCR · 2021Article
- Early-Life and Adult Adiposity, Adult Height, and Benign Breast Tissue Composition.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
Corrections and comments
- Erratum issuedCorrigendum.2021
Authors and funding
13 authors at 4 institutions in 3 countries.
Funding
Abstract
Background: New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses' Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC. Methods: Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided. Results: Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all Conclusions: The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.