ArticleIEEE transactions on medical imaging2016
Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development.
Article in IEEE transactions on medical imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses 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
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature.Cancer causes & control : CCC · 2023Pooled 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
- An Assessment of the Predictive Performance of Current Machine Learning-Based Breast Cancer Risk Prediction Models: Systematic Review.JMIR public health and surveillance · 2022Pooled it
- Subtraction of Temporally Sequential Digital Mammograms: Prediction and Localization of Near-Term Breast Cancer Occurrence.Journal of imaging informatics in medicine · 2025Article
- The clinical validity of radiomics-based prediction of molecular subtypes in breast cancer from digital mammary tomosynthesis.Frontiers in oncology · 2025Article
- Clinical Significance of Combined Density and Deep-Learning-Based Texture Analysis for Stratifying the Risk of Short-Term and Long-Term Breast Cancer in Screening.Diagnostics (Basel, Switzerland) · 2024Article
- Modeling correlated pairs of mammogram images.Statistics in medicine · 2024Article
- Prediction of Short-Term Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four-View Mammograms.Journal of digital imaging · 2022Article
- Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms.Diagnostics (Basel, Switzerland) · 2022Article
- Developing new quantitative CT image markers to predict prognosis of acute ischemic stroke patients.Journal of X-ray science and technology · 2022Article
- Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches.Journal of imaging · 2021Review
- Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment.Medical & biological engineering & computing · 2021Article
- Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification.Diagnostics (Basel, Switzerland) · 2020Article
- Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases.IEEE transactions on medical imaging · 2020Article
- Automated volumetric breast density measures: differential change between breasts in women with and without breast cancer.Breast cancer research : BCR · 2019Article
- Detection of potential microcalcification clusters using multivendor for-presentation digital mammograms for short-term breast cancer risk estimation.Medical physics · 2019Review
- Developing global image feature analysis models to predict cancer risk and prognosis.Visual computing for industry, biomedicine, and art · 2019Article
- Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms.Annals of biomedical engineering · 2018Article
- Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk.Physics in medicine and biology · 2018Article
- Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.Physics in medicine and biology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The purpose of this study is to develop and test a new computerized model for predicting near-term breast cancer risk based on quantitative assessment of bilateral mammographic image feature variations in a series of negative full-field digital mammography (FFDM) images. The retrospective dataset included series of four sequential FFDM examinations of 335 women. The last examination in each series ("current") and the three most recent "prior" examinations were obtained. All "prior" examinations were interpreted as negative during the original clinical image reading, while in the "current" examinations 159 cancers were detected and pathologically verified and 176 cases remained cancer-free. From each image, we initially computed 158 mammographic density, structural similarity, and texture based image features. The absolute subtraction value between the left and right breasts was selected to represent each feature. We then built three support vector machine (SVM) based risk models, which were trained and tested using a leave-one-case-out based cross-validation method. The actual features used in each SVM model were selected using a nested stepwise regression analysis method. The computed areas under receiver operating characteristic curves monotonically increased from 0.666±0.029 to 0.730±0.027 as the time-lag between the "prior" (3 to 1) and "current" examinations decreases. The maximum adjusted odds ratios were 5.63, 7.43, and 11.1 for the three "prior" (3 to 1) sets of examinations, respectively. This study demonstrated a positive association between the risk scores generated by a bilateral mammographic feature difference based risk model and an increasing trend of the near-term risk for having mammography-detected breast cancer.
Indexed as
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.