A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Ger, Rachel B.; Mohamed, A.S.R.; Awan, Musaddiq J.; Ding, Yao; Li, Kimberly; Fave, Xenia; Beers, Andrew; Driscoll, Brandon; Elhalawani, Hesham; Hormuth, David A.; Houdt, Petra J. van; He, Renjie; Zhou, Shouhao; Mathieu, Kelsey Boitnott; Li, Heng; Coolens, Catherine; Chung, Caroline; Bankson, James A.; Huang, Wei; Wang, Jihong; Sandulache, Vlad C.; Lai, Stephen Y.; Howell, Rebecca M.; Stafford, R. Jason; Yankeelov, Thomas E.; Heide, Uulke A. van der; Frank, Steven J.; Barboriak, Daniel P.; Hazle, John D.; Court, Laurence Edward; Kalpathy–Cramer, Jayashree; Fuller, Clifton D.

doi:10.1038/s41598-017-11554-w

Cited by 31 publications

(13 citation statements)

References 47 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, Cron et al 20 demonstrated that larger percentages of unphysical values within regions are computed by algorithms when more noise is present in the DCE-MRI images. The inter-algorithm differences and noise dependence are a hindrance to the clinical implementation of DCE-MRI and prompt thorough investigation towards establishing better repeatability and reproducibility between sites before multi-institutional clinical trials of HNSCC patients with DCE-MRI 21 .…”

Section: Background and Summarymentioning

confidence: 99%

Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers

et al. 2018

View full text Add to dashboard Cite

Dynamic myraidpro contrast-enhanced magnetic resonance imaging (DCE-MRI) has been correlated with prognosis in head and neck squamous cell carcinoma as well as with changes in normal tissues. These studies implement different software, either commercial or in-house, and different scan protocols. Thus, the generalizability of the results is not confirmed. To assist in the standardization of quantitative metrics to confirm the generalizability of these previous studies, this data descriptor delineates in detail the DCE-MRI digital imaging and communications in medicine (DICOM) files with DICOM radiation therapy (RT) structure sets and digital reference objects (DROs), as well as, relevant clinical data that encompass a data set that can be used by all software for comparing quantitative metrics. Variable flip angle (VFA) with six flip angles and DCE-MRI scans with a temporal resolution of 5.5 s were acquired in the axial direction on a 3T MR scanner with a field of view of 25.6 cm, slice thickness of 4 mm, and 256×256 matrix size.

show abstract

Section: Background and Summarymentioning

confidence: 99%

Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Additionally, acquisition and analysis protocols to reduce uncertainty in the imaging measurements need to be developed. We [ 161 , 162 , 163 ] and others [ 164 , 165 , 166 , 167 ] have begun to demonstrate that quantitative imaging techniques to quantify tissue vascularity can be performed with high accuracy and precision. Furthermore, we have shown that certain MRI measures can be collected with high quality in the community setting (and not in a research or academic setting) using widely available hardware [ 28 ].…”

Section: Future Directionsmentioning

confidence: 99%

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

Hormuth

Phillips

et al. 2021

Cancers

View full text Add to dashboard Cite

Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2–3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.

show abstract

“…This challenge calls for the need to devote serious efforts in standardization of the complete process affecting the imaging biomarkers. 223 , 224 , 225 , 226 , 227 …”

Section: Mr Biomarkers Associated Biologic and Genetic Processes And Imaging Techniquesmentioning

confidence: 99%

Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294

et al. 2021

View full text Add to dashboard Cite

A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques 1 Med. Phys.

show abstract

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Cited by 31 publications

References 47 publications

Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers

Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294

Contact Info

Product

Resources

About