ReviewBreast (Edinburgh, Scotland)2020
Overview of radiomics in breast cancer diagnosis and prognostication.
Review in Breast (Edinburgh, Scotland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers, 5 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
159 citing papers in PubMed, 5 syntheses or guidelines pooled it, 301 citations in OpenAlex.
- Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2026Pooled it
- Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review.BMC bioinformatics · 2023Pooled it
- Application of radiomics to meningiomas: A systematic review.Neuro-oncology · 2023Pooled it
- PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Prediction of distant metastasis in esophageal cancer using a radiomics-clinical model.European journal of medical research · 2022Trial
- Association ofMolecular imaging and radionuclide therapy · 2026Article
- Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible.Journal of imaging informatics in medicine · 2026Article
- Interpretable Whole-Breast Radiomic Biomarkers for Exploratory Assessment of HER2 + Breast Cancer in Digital Mammography.Journal of imaging informatics in medicine · 2026Article
- Augmented Prediction of N Parameter in Breast Cancer: Is It Possible with Shear-Wave Elastography Ultrasound Radiomics?Cancers · 2026Article
- MRI-based deep learning radiomics in predicting histological differentiation of oropharyngeal cancer: a multicenter cohort study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Ultrasound-based attention-guided deep learning combined with radiomics to predict axillary lymph node metastasis in breast cancer.BMC medical imaging · 2026Article
- The potential association between degree of mammographic spiculation and prognosis.Insights into imaging · 2026Article
- TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images.Journal of imaging informatics in medicine · 2026Article
- Digital breast tomosynthesis-based radiomics for prediction of prognosis in breast cancer: a multicenter study.Gland surgery · 2026Article
- The Great Potential of DNA Methylation in Triple-Negative Breast Cancer: From Biological Basics to Clinical Application.Biomedicines · 2026Review
- Advanced radiomics techniques applied on carotid duplex ultrasound data predict adverse outcomes in acute ischemic stroke.Frontiers in neurology · 2026Article
- Artificial intelligence improves risk stratification for breast cancer recurrence and mortality in women exposed to pesticides: a call for reassessment of stratification criteria.Frontiers in oncology · 2026Article
- Integrated Multi-Omics and Spatial Transcriptomics Reveal GUK1 as a Prognostic Biomarker Regulated by the TP53-HSF1 Axis in Breast Cancer.Oncology research · 2026Article
- Multimodal feature fusion model for breast mass malignant risk stratification.Frontiers in oncology · 2026Article
99 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication.
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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.