ArticleIEEE transactions on medical imaging2020
Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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
17 citing papers in PubMed, 39 citations in OpenAlex.
- High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4.BMC medical imaging · 2023Trial
- Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme.Biomedical physics & engineering express · 2021Trial
- Toward Digital Twins for Optimal Radioembolization.PET clinics · 2026Review
- Exploratory Research of Intraductal Chemotherapy to Control the Focal Breast Lesion of Breast Cancer.Breast cancer (Dove Medical Press) · 2025Article
- Computational fluid dynamics modeling and simulation of nanoparticle-tumor interaction: Systematic literature review.Computational and structural biotechnology journal · 2025Review
- Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Current status in spatiotemporal analysis of contrast-based perfusion MRI.Magnetic resonance in medicine · 2024Review
- A global sensitivity analysis of a mechanistic model of neoadjuvant chemotherapy for triple negative breast cancer constrained by in vitro and in vivo imaging data.Engineering with computers · 2024Article
- Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics.IEEE transactions on bio-medical engineering · 2022Article
- Article
- Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.Biophysics reviews · 2022Review
- Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics.Magnetic resonance imaging · 2022Article
- Theranostic digital twins for personalized radiopharmaceutical therapies: Reimagining theranosticsFrontiers in oncology · 2022Article
- An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom.Medical image analysis · 2021Article
- Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data.Cancers · 2021Review
- Imaging-based characterization of convective tissue properties.International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group · 2020Article
- Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities.Journal of clinical medicine · 2020Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculature-interstitium geometry and realistic material properties, using dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) data. These data are used to constrain CFD simulations for determining the tumor-associated blood supply and interstitial transport characteristics unique to each patient. We then perform a proof-of-principle statistical comparison between these hemodynamic characteristics in 11 malignant and 5 benign lesions from 12 patients. Significant differences between groups (i.e., malignant versus benign) were observed for the median of tumor-associated interstitial flow velocity ( P = 0.028 ), and the ranges of tumor-associated blood pressure (P = 0.016) and vascular extraction rate (P = 0.040). The implication is that malignant lesions tend to have larger magnitude of interstitial flow velocity, and higher heterogeneity in blood pressure and vascular extraction rate. Multivariable logistic models based on combinations of these hemodynamic data achieved excellent differentiation between malignant and benign lesions with an area under the receiver operator characteristic curve of 1.0, sensitivity of 1.0, and specificity of 1.0. This image-based model system is a fundamentally new way to map flow and pressure fields related to breast tumors using only non-invasive, clinically available imaging data and established laws of fluid mechanics. Furthermore, the results provide preliminary evidence for this methodology's utility for the quantitative characterization of breast cancer.
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Registered trials
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.