scholarly journals Dynamic Contrast-Enhanced Imaging as a Prognostic Tool in Early Diagnosis of Prostate Cancer: Correlation with PSA and Clinical Stage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Xingchen Wu ◽  
Petri Reinikainen ◽  
Mika Kapanen ◽  
Tuula Vierikko ◽  
Pertti Ryymin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Early Diagnosis ◽  
Early Stage ◽  
Clinical Stage ◽  
Specific Antigen ◽  
Transfer Constant ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Background and Purpose. Although several methods have been developed to predict the outcome of patients with prostate cancer, early diagnosis of individual patient remains challenging. The aim of the present study was to correlate tumor perfusion parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical prognostic factors and further to explore the diagnostic value of DCE-MRI parameters in early stage prostate cancer. Patients and Methods. Sixty-two newly diagnosed patients with histologically proven prostate adenocarcinoma were enrolled in our prospective study. Transrectal ultrasound-guided biopsy (12 cores, 6 on each lobe) was performed in each patient. Pathology was reviewed and graded according to the Gleason system. DCE-MRI was performed and analyzed using a two-compartmental model; quantitative parameters including volume transfer constant (Ktrans), reflux constant (Kep), and initial area under curve (iAUC) were calculated from the tumors and correlated with prostate-specific antigen (PSA), Gleason score, and clinical stage. Results. Ktrans (0.11 ± 0.02 min−1 versus 0.16 ± 0.06 min−1; p<0.05), Kep (0.38 ± 0.08 min−1 versus 0.60 ± 0.23 min−1; p<0.01), and iAUC (14.33 ± 2.66 mmoL/L/min versus 17.40 ± 5.97 mmoL/L/min; p<0.05) were all lower in the clinical stage T1c tumors (tumor number, n=11) than that of tumors in clinical stage T2 (n=58). Serum PSA correlated with both tumor Ktrans (r=0.304, p<0.05) and iAUC (r=0.258, p<0.05). Conclusions. Our study has confirmed that DCE-MRI is a promising biomarker that reflects the microcirculation of prostate cancer. DCE-MRI in combination with clinical prognostic factors may provide an effective new tool for the basis of early diagnosis and treatment decisions.

Diagnostic performance of diffusion-weighted imaging combined with dynamic contrast-enhanced magnetic resonance imaging for prostate cancer: a systematic review and meta-analysis

2020 ◽  
pp. 028418512095626
Author(s):  
Lu Yang ◽  
Yuchuan Tan ◽  
Hanli Dan ◽  
Lin Hu ◽  
Jiuquan Zhang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Likelihood Ratio ◽  
Diffusion Weighted Imaging ◽  
Diagnostic Performance ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Background The diagnostic performance of diffusion-weighted imaging (DWI) combined with dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for the detection of prostate cancer (PCa) has not been studied systematically to date. Purpose To investigate the value of DWI combined with DCE-MRI quantitative analysis in the diagnosis of PCa. Material and Methods A systematic search was conducted through PubMed, MEDLINE, the Cochrane Library, and EMBASE databases without any restriction to language up to 10 December 2019. Studies that used a combination of DWI and DCE-MRI for diagnosing PCa were included. Results Nine studies with 778 participants were included. The combination of DWI and DCE-MRI provide accurate performance in diagnosing PCa with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratios of 0.79 (95% confidence interval [CI] = 0.76–0.81), 0.85 (95% CI = 0.83–0.86), 6.58 (95% CI = 3.93–11.00), 0.24 (95% CI = 0.17–0.34), and 36.43 (95% CI = 14.41–92.12), respectively. The pooled area under the summary receiver operating characteristic curve was 0.9268. Moreover, 1.5-T MR scanners demonstrated a slightly better performance than 3.0-T scanners. Conclusion Combined DCE-MRI and DWI could demonstrate a highly accurate area under the curve, sensitivity, and specificity for detecting PCa. More studies with large sample sizes are warranted to confirm these results.

scholarly journals Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Dong Wang ◽  
Lori R. Arlinghaus ◽  
Thomas E. Yankeelov ◽  
Xiaoping Yang ◽  
David S. Smith
Keyword(s):  
Compressed Sensing ◽  
Volume Fraction ◽  
Extracellular Volume ◽  
Pharmacokinetic Parameters ◽  
Transfer Constant ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Space Data

Purpose. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast. Methods. We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGVα2), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes. Results. NN produced the lowest image error (SER: 29.1), while TV/TGVα2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799). Conclusion. TV/TGVα2 should be used as temporal constraints for CS DCE-MRI of the breast.

Dynamic contrast enhanced MRI (DCE MRI) for assessment of effects of anti-angiogenic therapy: Comparison of the transfer constant (Ktrans) to blood flow and permeability derived by a distributed parameters model

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 14109-14109 ◽  
Author(s):  
C. H. Thng ◽  
T. S. Koh ◽  
H. Rumpel ◽  
J. B. Khoo ◽  
A. B. Ong ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Blood Flow ◽  
Phase I ◽  
Roc Analysis ◽  
Drug Exposure ◽  
Transfer Constant ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Distributed Parameters ◽  
Contrast Enhanced

14109 Background: Transfer constant (Ktrans) and IAUC60 normalized with arterial input function are commonly used dynamic contrast enhanced magnetic resonance imaging (DCE MRI) parameters. The distributed parameters model (DP) is a DCE MRI model that enables derivation of blood flow and capillary permeability-surface area product (PS). We aim to study the distributed parameters model as an alternative method of angiogenesis assessment and correlate the above parameters to drug exposure and patient outcome in a Phase I anti- angiogenic trial. Methods: Fifteen evaluable patients from an on-going Phase I trial (ABT 869) with 3 dose escalations formed the study population. Pharmacokinetic study was performed on Day 1 and the area under the concentration time curve extrapolated to infinity (AUCinfinity) was used as an indicator of drug exposure. All patients underwent DCE MRI at baseline, Day 3 and Day 15 with temporal resolution of 4 seconds. Gadolinium concentrations were estimated using a dual flip angle method. Patients demonstrating progressive disease in first 2 evaluation scans (cycle 2 or 4) based on RECIST criteria were considered progressors and all other patients non-progressors. Receiver operating curve (ROC) analysis was performed. Correlation with AUCinfinity was analyzed. Results: There is good correlation (Spearman’s coefficient -0.67, p = 0.008) between AUCinfinity and DP derived PS and less strong correlation with normalized IAUC60 (Spearman’s coefficient -0.57, p = 0.03). There is no correlation for Ktrans (Spearman’s coefficient 0.04). ROC analysis for predicting progressors versus non-progressors showed a higher ROC area for PS compared to Ktrans (0.83 versus 0.47, p = 0.037). Normalized IAUC60 showed a slightly lower area compared to PS (0.77 versus 0.83) but the difference is not significant (p = 0.58). Conclusions: PS derived from DP model shows better correlation with drug exposure and may predict patient outcome better than Ktrans. [Table: see text]

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