ArticleClinical breast cancer2021
Dynamic Changes of Convolutional Neural Network-based Mammographic Breast Cancer Risk Score Among Women Undergoing Chemoprevention Treatment.
Article in Clinical breast cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Artificial intelligence in the radiologic assessment of ductal carcinomaFrontiers in oncology · 2026Pooled it
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled it
- Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.JNCI cancer spectrum · 2024Trial
- Multimodal Machine Learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis.Cancer management and research · 2024Article
- Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.Tomography (Ann Arbor, Mich.) · 2023Review
- Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.Pattern recognition · 2022Article
- Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types.Cancers · 2022Article
- Use of a convolutional neural network-based mammographic evaluation to predict breast cancer recurrence among women with hormone receptor-positive operable breast cancer.Breast cancer research and treatment · 2022Article
- Deep learning in breast imaging.BJR open · 2022Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
introductionWe investigated whether our convolutional neural network (CNN)-based breast cancer risk model is modifiable by testing it on women who had undergone risk-reducing chemoprevention treatment. MATERIALS AND
methodsWe conducted a retrospective cohort study of patients diagnosed with atypical hyperplasia, lobular carcinoma in situ, or ductal carcinoma in situ at our institution from 2007 to 2015. The clinical characteristics, chemoprevention use, and mammography images were extracted from the electronic health records. We classified two groups according to chemoprevention use. Mammograms were performed at baseline and subsequent follow-up evaluations for input to our CNN risk model. The 2 chemoprevention groups were compared for the risk score change from baseline to follow-up. The change categories included stayed high risk, stayed low risk, increased from low to high risk, and decreased from high to low risk. Unordered polytomous regression models were used for statistical analysis, with P < .05 considered statistically significant.
resultsOf 541 patients, 184 (34%) had undergone chemoprevention treatment (group 1) and 357 (66%) had not (group 2). Using our CNN breast cancer risk score, significantly more women in group 1 had shown a decrease in breast cancer risk compared with group 2 (33.7% vs. 22.9%; P < .01). Significantly fewer women in group 1 had an increase in breast cancer risk compared with group 2 (11.4% vs. 20.2%; P < .01). On multivariate analysis, an increase in breast cancer risk predicted by our model correlated negatively with the use of chemoprevention treatment (P = .02).
conclusionsOur CNN-based breast cancer risk score is modifiable with potential utility in assessing the efficacy of known chemoprevention agents and testing new chemoprevention strategies.
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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.