ArticleJournal of magnetic resonance imaging : JMRI2019
Characterization of Sub-1 cm Breast Lesions Using Radiomics Analysis.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Preoperative Prediction Power of Radiomics for Breast Cancer: A Systemic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters.Clinical and experimental medicine · 2023Trial
- Distinguishing Benign from Malignant Small Enhancing Breast Lesions Using Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- External validation of multiparametric magnetic resonance imaging-based decision rules for characterizing breast lesions and comparison to Kaiser score and breast imaging reporting and data system (BI-RADS) category.Quantitative imaging in medicine and surgery · 2025Article
- Research on the application of distinguishing between benign and malignant breast nodules using MRI and US radiomics.Frontiers in oncology · 2025Article
- Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches.Journal of personalized medicine · 2023Article
- Article
- Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM).Diagnostics (Basel, Switzerland) · 2023Article
- Combined Use of Texture Features and Morphological Classification Based on Dynamic Contrast-enhanced MR Imaging: Differentiating Benign and Malignant Breast Masses with High Negative Predictive Value.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma.The British journal of radiology · 2022Article
- Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm).European radiology · 2022Article
- Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.La Radiologia medica · 2022Review
- Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths.Magnetic resonance imaging · 2021Article
- Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2021Article
- Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.Frontiers in oncology · 2021Article
- A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy.Frontiers in genetics · 2021Article
- Machine learning in breast MRI.Journal of magnetic resonance imaging : JMRI · 2020Review
- Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics.Journal of clinical medicine · 2020Article
- Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer.Breast cancer research : BCR · 2020Article
- Contrast-Enhanced Spectral Mammography-Based Radiomics Nomogram for Identifying Benign and Malignant Breast Lesions of Sub-1 cm.Frontiers in oncology · 2020Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSmall breast lesions are difficult to visually categorize due to the inherent lack of morphological and kinetic detail. PURPOSE: To assess the efficacy of radiomics analysis in discriminating small benign and malignant lesions utilizing model free parameter maps. STUDY TYPE: Retrospective, single center. POPULATION: In all, 149 patients, with a total of 165 lesions scored as BI-RADS 4 or 5 on MRI, with an enhancing volume of <0.52 cm FIELD STRENGTH/SEQUENCE: Higher spatial resolution T ASSESSMENT: Parameter maps reflecting initial enhancement, overall enhancement, area under the enhancement curve, and washout were generated. Heterogeneity measures based on first-order statistics, gray level co-occurrence matrices, run length matrices, size zone matrices, and neighborhood gray tone difference matrices were calculated. Data were split into a training dataset (~75% of cases) and a test dataset (~25% of cases). STATISTICAL TESTS: Comparison of medians was assessed using the nonparametric Mann-Whitney U-test. The Spearman rank correlation coefficient was utilized to determine significant correlations between individual features. Finally, a support vector machine was employed to build multiparametric predictive models.
resultsUnivariate analysis revealed significant differences between benign and malignant lesions for 58/133 calculated features (P < 0.05). Support vector machine analysis resulted in areas under the curve (AUCs) ranging from 0.75-0.81. High negative (>89%) and positive predictive values (>83%) were found for all models. DATA
conclusionRadiomics analysis of small contrast-enhancing breast lesions is of value. Texture features calculated from later timepoints on the enhancement curve appear to offer limited additional value when compared with features determined from initial enhancement for this patient cohort. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1468-1477.
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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.