ArticleScientific reports2020
MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Trial
- Comprehensive Radiomics Analysis to Enhance Breast Lesion Characterization Using QUS Spectral Parametric Imaging.Cancers · 2026Article
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Inter-observer variability in breast segmentation and its impact on focused ultrasound thermal therapy modeling.Physics in medicine and biology · 2026Article
- The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.Cancer biology & medicine · 2026Review
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Radiomics-based Machine Learning Models for Classifying Breast Cancer on Dynamic-Contrast Enhanced MRI through Multi-Observer AnalysisCurrent medical imaging · 2026Article
- Article
- Multiomics Machine Learning to Predict Neoadjuvant Chemotherapy Outcome and Relapse of Breast Cancer.BME frontiers · 2026Article
- Review
- Segmentation variability and radiomics stability for predicting triple-negative breast cancer subtype using magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Optimizing meningioma grading with radiomics and deep features integration, attention mechanisms, and reproducibility analysis.European journal of medical research · 2025Article
- Article
- Reproducibility and interpretability in radiomics: a critical assessmentDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Superior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study.La Radiologia medica · 2025Observational
- Radiomic Analysis of Magnetic Resonance Imaging for Breast Cancer withDiagnostics (Basel, Switzerland) · 2025Article
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Radiomics in breast cancer: Current advances and future directions.Cell reports. Medicine · 2024Review
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19-0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability.
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