ArticleDiagnostics (Basel, Switzerland)2020
Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 48 citations in OpenAlex.
- How to optimise breast cancer staging with contrast-enhanced mammography: current evidence and clinical implications.Insights into imaging · 2026Review
- Clinical Applications and Decision-Making Impact of Contrast-Enhanced Mammography and MRI: A Recent Structured Review.Diagnostics (Basel, Switzerland) · 2026Review
- Development and validation of an MRI-based deep learning system for triple-class ER expression classification in breast cancer: a large-scale multicenter study.BMC medicine · 2026Article
- Clinically Robust Deep Learning for Contrast-Enhanced Mammography: Multicenter Evaluation Across Convolutional Neural Network Architectures.Bioengineering (Basel, Switzerland) · 2026Article
- Engineering the Image Representation for Deep Learning in Contrast-Enhanced Mammography: A Systematic Analysis of Preprocessing and Anatomical Masking.Bioengineering (Basel, Switzerland) · 2026Article
- Review
- Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization.Bioengineering (Basel, Switzerland) · 2025Article
- Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients.Breast cancer (Dove Medical Press) · 2025Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.La Radiologia medica · 2024Article
- State-of-the-art for contrast-enhanced mammography.The British journal of radiology · 2024Review
- Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome.La Radiologia medica · 2023Article
- Invasive Lobular Carcinoma: A Review of Imaging Modalities with Special Focus on Pathology Concordance.Healthcare (Basel, Switzerland) · 2023Review
- Tumor Lung Visualization and Localization through Virtual Reality and Thermal Feedback Interface.Diagnostics (Basel, Switzerland) · 2023Article
- An ultrasound-based radiomics model to distinguish between sclerosing adenosis and invasive ductal carcinoma.Frontiers in oncology · 2023Article
- Article
- Contrast-Enhanced Mammography versus Breast Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Article
- Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions.Current oncology (Toronto, Ont.) · 2022Article
- Association between quantitative and qualitative image features of contrast-enhanced mammography and molecular subtypes of breast cancer.Quantitative imaging in medicine and surgery · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 5 countries.
Funding
Abstract
The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR-), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR- breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR- breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.
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.