ArticleMedical physics2018
Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.
Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Imaging features of gadoxetic acid-enhanced MR imaging for evaluation of tumor-infiltrating CD8 cells and PD-L1 expression in hepatocellular carcinoma.Cancer immunology, immunotherapy : CII · 2022Trial
- Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk.BMC medical imaging · 2026Article
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- A predictive model for differentiating PFA and PFB subtypes of posterior fossa ependymoma using multi-sequence MRI radiomics: a two-center study.Neuroradiology · 2026Article
- Post-processing steps improve generalisability and robustness of an MRI-based radiogenomic model for human papillomavirus status prediction in oropharyngeal cancer.European radiology · 2025Article
- TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.Medical image analysis · 2025Article
- GPT-Driven Radiology Report Generation with Fine-Tuned Llama 3.Bioengineering (Basel, Switzerland) · 2024Article
- Prediction of High-Risk Neuroblastoma Among Neuroblastic Tumors Using Radiomics Features Derived from Magnetic Resonance Imaging: A Pilot Study.Yonsei medical journal · 2024Article
- Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability.Scientific reports · 2023Article
- Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation.Journal of personalized medicine · 2023Article
- Harmonization Strategies in Multicenter MRI-Based Radiomics.Journal of imaging · 2022Review
- Robustness of radiomics to variations in segmentation methods in multimodal brain MRI.Scientific reports · 2022Article
- Challenges in ensuring the generalizability of image quantitation methods for MRI.Medical physics · 2022Review
- Precision of MRI radiomics features in the liver and hepatocellular carcinoma.European radiology · 2022Article
- Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm).European radiology · 2022Article
- Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.Quantitative imaging in medicine and surgery · 2021Review
- Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.Scientific reports · 2021Article
- Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.European journal of cancer (Oxford, England : 1990) · 2021Article
- Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas.Frontiers in neurology · 2021Article
- Machine Learning Based on Multi-Parametric MRI to Predict Risk of Breast Cancer.Frontiers in oncology · 2021Article
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Abstract
purposeTo review features used in MRI radiomics of breast cancer and study the inter-reader stability of the features.
methodsWe implemented 529 algorithmic features that can be extracted from tumor and fibroglandular tissue (FGT) in breast MRIs. The features were identified based on a review of the existing literature with consideration of their usage, prognostic ability, and uniqueness. The set was then extended so that it comprehensively describes breast cancer imaging characteristics. The features were classified into 10 groups based on the type of data used to extract them and the type of calculation being performed. For the assessment of inter-reader variability, four fellowship-trained readers annotated tumors on preoperative dynamic contrast-enhanced MRIs for 50 breast cancer patients. Based on the annotations, an algorithm automatically segmented the image and extracted all features resulting in one set of features for each reader. For a given feature, the inter-reader stability was defined as the intraclass correlation coefficient (ICC) computed using the feature values obtained through all readers for all cases.
resultsThe average inter-reader stability for all features was 0.8474 (95% CI: 0.8068-0.8858). The mean inter-reader stability was lower for tumor-based features (0.6348, 95% CI: 0.5391-0.7257) than FGT-based features (0.9984, 95% CI: 0.9970-0.9992). The feature group with the highest inter-reader stability quantifies breast and FGT volume. The feature group with the lowest inter-reader stability quantifies variations in tumor enhancement.
conclusionsBreast MRI radiomics features widely vary in terms of their stability in the presence of inter-reader variability. Appropriate measures need to be taken for reducing this variability in tumor-based radiomics.
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