ReviewJournal of imaging2021
Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches.
Review in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Knowledge, attitude, and perception of Pakistani women regarding breast cancer and mammography screening: A cross-sectional study.Medicine · 2025Article
- Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers.Scientific reports · 2025Article
- Breast density analysis of digital breast tomosynthesis.Scientific reports · 2023Article
- Breast Density Analysis Using Digital Breast Tomosynthesis.bioRxiv : the preprint server for biology · 2023Article
- Mouse Mammary Gland Whole Mount Density Assessment across Different Morphologies Using a Bifurcated Program for Image Processing.The American journal of pathology · 2022Article
- AI in Breast Cancer Imaging: A Survey of Different Applications.Journal of imaging · 2022Review
- Advanced Computational Methods for Oncological Image Analysis.Journal of imaging · 2021Article
- Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice.
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.