ArticleRadiology2019
Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set.
Article in Radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Preoperative Prediction Power of Radiomics for Breast Cancer: A Systemic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Development and internal validation of mammography feature-based prognostic models for distant recurrence-free survival of invasive breast cancer in a screening cohort.NPJ breast cancer · 2026Article
- Radiomics-based mammographic abnormality identification via radiologist annotations.BJR artificial intelligence · 2026Article
- Artificial intelligence integrates multi-omics data for precision stratification and drug resistance prediction in breast cancer.Frontiers in oncology · 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
- Use of Response Permutation to Measure an Imaging Dataset's Susceptibility to Overfitting by Selected Standard Analysis Pipelines.Academic radiology · 2024Article
- Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer.Scientific reports · 2024Article
- Review
- Performance metric curve analysis framework to assess impact of the decision variable threshold, disease prevalence, and dataset variability in two-class classification.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- A Radiomics Nomogram for Distinguishing Benign From Malignant Round-Like Breast Tumors.Frontiers in oncology · 2022Article
- Deep learning model improves radiologists' performance in detection and classification of breast lesions.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2021Article
- Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML).Diagnostics (Basel, Switzerland) · 2021Review
- A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs.Nature communications · 2021Article
- Magnetic Resonance Imaging Radiomics Analyses for Prediction of High-Grade Histology and Necrosis in Clear Cell Renal Cell Carcinoma: Preliminary Experience.Clinical genitourinary cancer · 2021Article
- Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging.Medical physics · 2021Article
- Microscopic Tumour Classification by Digital Mammography.Journal of healthcare engineering · 2021Article
- Multiparameter MRI Radiomics Model Predicts Preoperative Peritoneal Carcinomatosis in Ovarian Cancer.Frontiers in oncology · 2021Article
- Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions.Communications medicine · 2021Article
- Breast cancer screening: in the era of personalized medicine, age is just a number.Quantitative imaging in medicine and surgery · 2020Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV
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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.