Purpose: To investigate the role of the quantitative parameters of dynamic contrast-enhanced MR imaging (DCE-MRI) in the prediction of the response to chemotherapy in pancreatic ductal carcinoma (PDC).
Method: Forty patients with histologically confirmed PDC who underwent quantitative DCE-MRI were retrospectively analyzed. All patients were divided into groups of responders and nonresponders. DCE-MRI parameters, including the volume transfer constant (K), the extracellular extravascular volume fraction (v), the rate constant (k) and the initial area under the concentration curve in 60 s (iAUC60), were measured and compared. DCE-MRI parameters were obtained from different ROIs.
Results: The values of K in responders with peripheral, whole tumor slice, and adjacent non-tumorous region ROIs were significantly higher than those in nonresponders (P = 0.015, 0.043, and 0.025, respectively). Responders showed a significantly higher k with peripheral area ROI compared with nonresponders (P = 0.013). Ve and iAUC60 with all ROIs were not significantly different between responders and nonresponders (P = 0.140-0.968). Kep with periphery ROI showed the highest area under the ROC curve (AUC) of 0.806, but there were no statistical differences when compared with values of K.There were statistically significant differences for DCE-MRI parameters among four ROIs (all P < 0.05). All parameters showed good to excellent intra and interobserver agreement.
Conclusions: Quantitative parameters derived from DCE-MRI might be a potential predictor of response to gemcitabine in patients with PDC. Perfusion parameters were diverse depending on the location of the ROI on different tumoral and peritumoral areas.
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http://dx.doi.org/10.1016/j.ejrad.2019.108734 | 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.
J 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 PDFFront Oncol
December 2024
Department of Magnetic Resonance Imaging (MRI), The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Purpose: The aim of this study was to evaluate the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived kinetic parameters with high spatiotemporal resolution in discriminating malignant from normal prostate tissues.
Methods: Fifty patients with suspicious of malignant diseases in prostate were included in this study. Regions of interest (ROI) were manually delineated by experienced radiologists.
Magn 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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