ArticleCancers2025
Preliminary Evaluation of Radiomics in Contrast-Enhanced Mammography for Prognostic Prediction of Breast Cancer.
Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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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.
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Who cites it
4 citing papers in PubMed.
- ROI-Based Quantitative Analysis in Contrast-Enhanced Mammography: A Feasibility Study for Reproducible Enhancement Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical Applications and Decision-Making Impact of Contrast-Enhanced Mammography and MRI: A Recent Structured Review.Diagnostics (Basel, Switzerland) · 2026Review
- Contrast-Enhanced Mammography and Deep Learning-Derived Malignancy Scoring in Breast Cancer Molecular Subtype Assessment.Medicina (Kaunas, Lithuania) · 2026Article
- 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
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRadiomics is changing clinical practice by providing quantitative information from images to improve diagnosis, prognosis, and treatment planning. This study aims to investigate a radiomics model developed from contrast-enhanced mammography (CEM) images to predict disease-free survival (DFS) and overall survival (OS) in breast cancer (BC) patients.
methodsFrom January 2013 to December 2015, all consecutive BC patients who underwent CEM before biopsy at a referral center were enrolled. Clinical data included histological results, receptor profiles, and follow-up (DFS and OS). A region of interest (ROI) of the enhancing lesion was selected from recombined CEM images by experienced radiologists, and radiomic features were extracted. A Cox-LASSO model assigned coefficients to the features, generating patient radiomic scores (RSs), which were dichotomized for graphical representation. Model performance was assessed using the C index.
resultsThe study included 126 BC patients with predominantly "mass"-type lesions (95%) and a median follow-up of 6.88 years (IQR 3.10-8.15). The median age of the patients at the time of examination was 49.2 years (IQR: [42.33-56.98]). Radiomic and clinical-radiomic models showed significant associations between RS, DFS, and OS, with patients with RS below the median showing a better prognosis (
conclusionsRadiomic analysis of CEM images may predict DFS and OS in BC patients, offering additional prognostic value beyond clinical models alone.
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