ArticleJournal of medical imaging (Bellingham, Wash.)2018
Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers' systems.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- More than density: validating a mammographic masking prediction model in Dutch breast cancer screening.European radiology · 2025Article
- Article
- A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions.Journal of personalized medicine · 2023Article
- Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction.Cancers · 2023Article
- Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation.Cancers · 2021Article
- Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Robust Radiomic Feature Selection in Digital Mammography: Understanding the Effect of Imaging Acquisition Physics Using Phantom and Clinical Data Analysis.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- NCTN Assessment on Current Applications of Radiomics in Oncology.International journal of radiation oncology, biology, physics · 2019Review
- Radiomics robustness assessment and classification evaluation: A two-stage method demonstrated on multivendor FFDM.Medical physics · 2019Article
- Article
- Deep learning in medical imaging and radiation therapy.Medical physics · 2019Review
- Variation in algorithm implementation across radiomics software.Journal of medical imaging (Bellingham, Wash.) · 2018Article
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Authors and funding
7 authors.
Funding
Abstract
The robustness of radiomic texture analysis across different manufacturers of mammography imaging systems is investigated. We quantified feature robustness across mammography manufacturers using a dataset of 111 women who underwent consecutive screening mammography on both general electric and Hologic systems. In each mammogram, a square region of interest (ROI) directly behind the nipple was manually selected. Radiomic features describing parenchymal patterns were automatically extracted on each ROI. Feature comparisons were conducted between manufacturers (and breast densities) using newly developed robustness metrics descriptive of correlation, equivalence, and variability. By examining the distribution of these metric values, we propose the following selection criteria to guide feature evaluation in this dataset: (1) [Formula: see text] of feature ratios [Formula: see text], (2) standard deviation of feature ratios [Formula: see text], (3) correlation of features [Formula: see text], and (4) [Formula: see text]. Statistically significant correlation coefficients ranged from 0.13 to 0.68 in comparisons between the two mammographic systems tested. Features describing spatial patterns tended to exhibit high correlation coefficients, while intensity- and directionality-based features had comparatively poor correlation. Our proposed robustness metrics may be used to evaluate other datasets, for which different ranges of metric values may be appropriate.
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