ArticleJNCI cancer spectrum2018
Cirrus: An Automated Mammography-Based Measure of Breast Cancer Risk Based on Textural Features.
Article in JNCI cancer spectrum, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature.Breast cancer research : BCR · 2022Pooled it
- Longitudinal changes in mammographic density and reproductive hormones during the menopausal transition in middle-aged Korean women: effect modification by obesity.Breast cancer research : BCR · 2025Article
- Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency.Cancer causes & control : CCC · 2024Article
- Causal relationships between breast cancer risk factors based on mammographic features.Breast cancer research : BCR · 2023Article
- Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks.International journal of epidemiology · 2023Article
- Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk.Cancer prevention research (Philadelphia, Pa.) · 2023Article
- Article
- Blood DNA methylation profiles improve breast cancer prediction.Molecular oncology · 2022Article
- Mammographic texture features associated with contralateral breast cancer in the WECARE Study.NPJ breast cancer · 2021Article
- RE: Chemopreventive Agents to Reduce Mammographic Breast Density in Premenopausal Women: A Systematic Review of Clinical Trials.JNCI cancer spectrum · 2021Article
- Age dependency of the polygenic risk score for colorectal cancer.American journal of human genetics · 2021Article
- Interval breast cancer risk associations with breast density, family history and breast tissue aging.International journal of cancer · 2020Article
- Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk.Journal of clinical medicine · 2020Article
- Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk.Breast cancer research : BCR · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe applied machine learning to find a novel breast cancer predictor based on information in a mammogram.
methodsUsing image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case-control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index.
resultsCombining studies, almost all textural features were associated with case-control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all
conclusionsA fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.
Identifiers
What OpenQuestion holds
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.