ArticleScientific reports2019
Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Pooled it
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Breath biopsy of breast cancer using sensor array signals and machine learning analysis.Scientific reports · 2021Trial
- A review of application of Artificial Intelligence in breast cancer detection and treatment.Discover oncology · 2026Review
- AI-powered radiogenomics: imaging-driven diagnosis and discovery of cancer's molecular and cellular landscape.Molecular genetics and genomics : MGG · 2026Review
- The value of multimodal ultrasound imaging in differentiating HER2-positive breast cancer.Frontiers in oncology · 2026Article
- Evaluation of the clinical characteristics and potentially prognostic factors in HER2-positive breast cancer.Amino acids · 2025Article
- Predicting Spinal Cord Injury Prognosis Using Machine Learning: Systematic Review and Meta-Analysis.JMIR AI · 2025Review
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Review
- A glimpse into the future: Integrating artificial intelligence for precision HER2-positive breast cancer management.iMetaOmics · 2024Review
- Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis.Interactive journal of medical research · 2024Article
- Machine learning analysis for the association between breast feeding and metabolic syndrome in women.Scientific reports · 2024Article
- Pathway-based analyses of gene expression profiles at low doses of ionizing radiation.Frontiers in bioinformatics · 2024Article
- Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.The British journal of radiology · 2023Review
- Comparison of CT- and MRI-Based Quantification of Tumor Heterogeneity and Vascularity for Correlations with Prognostic Biomarkers and Survival Outcomes: A Single-Center Prospective Cohort Study.Bioengineering (Basel, Switzerland) · 2023Article
- Machine learning-based risk factor analysis of necrotizing enterocolitis in very low birth weight infants.Scientific reports · 2022Article
- Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants.Scientific reports · 2022Article
- Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.Cancers · 2022Review
Corrections and comments
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8 authors.
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Abstract
Radiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of machine learning approaches to radiogenomics using low-dose perfusion computed tomography (CT) to predict prognostic biomarkers and molecular subtypes of invasive breast cancer. This prospective study enrolled a total of 723 cases involving 241 patients with invasive breast cancer. The 18 CT parameters of cancers were analyzed using 5 machine learning models to predict lymph node status, tumor grade, tumor size, hormone receptors, HER2, Ki67, and the molecular subtypes. The random forest model was the best model in terms of accuracy and the area under the receiver-operating characteristic curve (AUC). On average, the random forest model had 13% higher accuracy and 0.17 higher AUC than the logistic regression. The most important CT parameters in the random forest model for prediction were peak enhancement intensity (Hounsfield units), time to peak (seconds), blood volume permeability (mL/100 g), and perfusion of tumor (mL/min per 100 mL). Machine learning approaches to radiogenomics using low-dose perfusion breast CT is a useful noninvasive tool for predicting prognostic biomarkers and molecular subtypes of invasive breast cancer.
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