Purpose: Most dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data are evaluated for individual patients with cohorts analyzed to detect significant changes from baseline values, repeating the process at each posttreatment timepoint. Our study aimed to develop a statistically valid model for the complete time course of DCE-MRI data in a patient cohort.
Materials And Methods: Data from 10 patients with colorectal cancer liver metastases were analyzed, including two baseline scans and four post-bevacizumab scans. Apparent changes in tumor median K(trans) were adjusted for changes in observed enhancing tumor fraction (EnF) by multiplying K(trans) by EnF (KEnF). A mixed-effects model (MEM) was defined to describe the KEnF time course for all patients simultaneously by assuming a three-parameter indirect response model with model parameters lognormally distributed across patients.
Results: The typical cohort time course showed a KEnF reduction to 59% of baseline at 24 hours, returning to 65% of baseline values by day 12. Interpatient variability of model parameters ranged from 11% to 307%.
Conclusion: The MEM approach has potential for comparing responses at a group level in clinical trials with different doses, schedules, or combination regimens. Furthermore, the KEnF biomarker successfully resolved confounds in interpreting K(trans) arising from therapy induced changes in the volume of enhancing tumor.
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http://dx.doi.org/10.1002/jmri.24514 | DOI Listing |
Front Oncol
December 2024
Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Purpose: To explore the value of quantitative imaging parameters by enhanced T weighted angiography (ESWAN) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for evaluating the expression of Hypoxia-inducible factor-1α (HIF-1α) in endometrial carcinoma (EC).
Methods: Data from 122 patients with EC confirmed by clinical pathology were retrospectively analyzed. According to the number of positive cells stained with HIF-1α by immunohistochemistry, patients were divided into two groups: 65 cases with high expression of HIF-1α and 57 cases with low expression of HIF-1α.
iScience
December 2024
Department of Anesthesiology, Yale School of Medicine, New Haven, CT 06510, USA.
Brain waste clearance from the interstitial fluid environment is challenging to measure, which has contributed to controversy regarding the significance of glymphatic transport impairment for neurodegenerative processes. Dynamic contrast enhanced MRI (DCE-MRI) with cerebrospinal fluid administration of Gd-tagged tracers is often used to assess glymphatic system function. We previously quantified glymphatic transport from DCE-MRI data utilizing regularized optimal mass transport (rOMT) analysis, however, information specific to glymphatic clearance was not directly derived.
View Article and Find Full Text PDFJ Appl Clin Med Phys
December 2024
Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
Purpose: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.
Methods: DCE-MRI data for simulation studies were synthesized using the extended Tofts model and a population-averaged arterial input function (AIF). The ranges of PK parameters for training the RNNs were determined from data of patients with brain tumors.
J Control Release
December 2024
Department of Human Structure and Repair, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent CRIG, Ghent, Belgium. Electronic address:
Tumor fluid dynamics and drug delivery simulations in solid tumors are highly relevant topics in clinical oncology. The current study introduces a novel method combining computational fluid dynamics (CFD) modeling, quantitative magnetic resonance imaging (MRI; including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted (DW) MRI), and a novel ex-vivo protocol to generate patient-specific models of solid tumors in four patients with peritoneal metastases. DCE-MRI data were analyzed using the extended Tofts model to estimate the spatial distribution of tumor capillary permeability using the K parameter.
View Article and Find Full Text PDFMagn Reson Imaging
December 2024
Weill Cornell Graduate School of Medical Sciences, New York, NY, United States; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Department of Radiology, Memorial Sloan Kettering Cancer Center, NY, New York, USA.
Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption of quantitative DCE-MRI remains limited due to challenges in acquisition and quantification performance, and lack of automated tools. This study presents an end-to-end deep learning pipeline that exploits a novel deep reconstruction network called DCE-Movienet with a previously developed deep quantification network called DCE-Qnet for fast and quantitative DCE-MRI.
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