ArticleEuropean radiology experimental2020
A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI.
Article in European radiology experimental, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Characteristics and Pathologic Results of Foci on Breast Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Journal of magnetic resonance imaging : JMRI · 2025Pooled it
- Distinguishing Benign from Malignant Small Enhancing Breast Lesions Using Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance.Insights into imaging · 2023Article
- Machine Learning-Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features.Journal of digital imaging · 2022Article
- Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.La Radiologia medica · 2022Review
- Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions.European radiology · 2021Article
- Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature.
methodsForty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method ("training with input selection and testing") was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs).
resultsA k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87-100%), a specificity of 37/41 (90%, 95% CI 77-97%), and an accuracy of 64/68 (94%, 95% CI 86-98%).
conclusionThis preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.
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