scholarly journals Evaluation of Rabbits Liver Fibrosis Using Gd-DTPA-BMA of Dynamic Contrast-Enhanced Magnetic Resonance Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Qian Cui ◽  
FengTai He ◽  
Jiawei Hu ◽  
Shuo Li ◽  
Dongmei Guo ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Normal Liver ◽  
Fibrosis Stage ◽  
Pharmacokinetic Parameters ◽  
Control Group ◽  
Resonance Imaging ◽  
Fibrotic Liver ◽  
Dce Mri ◽  
Contrast Enhanced ◽  
Advanced Liver Fibrosis

Objective. To evaluate the different pharmacokinetic parameters of the DCE-MRI method on diagnosing and staging of rabbits’ liver fibrosis. Methods. We had performed DCE-MRI for rabbits that had been divided into the experiment group and the control group. Then, rabbits’ images were transferred to a work station to get three parameters such as Ktrans, Kep, and Ve, which had been measured to calculate. After data were analyzed, ROC analyses were performed to assess the diagnostic performance of Ktrans, Kep, and Ve to judge liver fibrosis. Results. The distribution of the different liver fibrosis group was as follows: F1, n = 8; F2, n = 9; F3, n = 6; F4, n = 5. No fibrosis was deemed as F0, n = 6. Kep is statistically significant P < 0.05 for F0 and mild liver fibrosis stage, and the Kep shows AUC of 0.814. Three parameters are statistically significant for F0 and advanced liver fibrosis stage (Ktrans and Kep, P < 0.01 ; Ve, P < 0.05 ), and the Ktrans shows AUC of 0.924; the Kep shows AUC of 0.909; the Ve shows AUC of 0.848; Ktrans and Kep are statistically significant for mild and advanced liver fibrosis stages (Ktrans, P < 0.01 ; Kep, P < 0.05 ), and the Ktrans shows AUC of 0.840; the Kep shows AUC of 0.765. Both Ktrans and Kep are negatively correlated with the liver fibrosis stage. Ve is positively correlated with the liver fibrosis stage. Conclusion. Ktrans is shown to be the best DCE parameter to distinguish the fibrotic liver from the normal liver and mild and advanced fibrosis. On the contrary, Kep is moderate and Ve is worst. And Kep is a good DCE parameter to differentiate mild fibrosis from the normal liver.

scholarly journals Quantitative Three-Dimensional Assessment of the Pharmacokinetic Parameters of Intra- and Peri-tumoural Tissues on Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging

2021 ◽  
Author(s):  
A. Niukkanen ◽  
H. Okuma ◽  
M. Sudah ◽  
P. Auvinen ◽  
A. Mannermaa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Three Dimensional ◽  
Pharmacokinetic Parameters ◽  
High Grade ◽  
Breast Cancers ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

AbstractWe aimed to assess the feasibility of three-dimensional (3D) segmentation and to investigate whether semi-quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters are associated with traditional prognostic factors for breast cancer. In addition, we evaluated whether both intra-tumoural and peri-tumoural DCE parameters can differentiate the breast cancers that are more aggressive from those that are less aggressive. Consecutive patients with newly diagnosed invasive breast cancer and structural breast MRI (3.0 T) were included after informed consent. Fifty-six patients (mean age, 57 years) with mass lesions of > 7 mm in diameter were included. A semi-automatic image post-processing algorithm was developed to measure 3D pharmacokinetic information from the DCE-MRI images. The kinetic parameters were extracted from time-signal curves, and the absolute tissue contrast agent concentrations were calculated with a reference tissue model. Markedly, higher intra-tumoural and peri-tumoural tissue concentrations of contrast agent were found in high-grade tumours (n = 44) compared to low-grade tumours (n = 12) at every time point (P = 0.006–0.040), providing positive predictive values of 90.6–92.6% in the classification of high-grade tumours. The intra-tumoural and peri-tumoural signal enhancement ratios correlated with tumour grade, size, and Ki67 activity. The intra-observer reproducibility was excellent. We developed a model to measure the 3D intensity data of breast cancers. Low- and high-grade tumours differed in their intra-tumoural and peri-tumoural enhancement characteristics. We anticipate that pharmacokinetic parameters will be increasingly used as imaging biomarkers to model and predict tumour behavior, prognoses, and responses to treatment.

Liver perfusion in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI): comparison of enhancement in Gd-BT-DO3A and Gd-EOB-DTPA in normal liver parenchyma

2014 ◽  
Vol 24 (9) ◽  
pp. 2146-2156 ◽  
Author(s):  
Hanke J. Schalkx ◽  
Marijn van Stralen ◽  
Kenneth Coenegrachts ◽  
Maurice A. A. J. van den Bosch ◽  
Charlotte S. van Kessel ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Liver Perfusion ◽  
Normal Liver ◽  
Liver Parenchyma ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Normal Liver Parenchyma ◽  
Contrast Enhanced

scholarly journals Noninvasive Assessment of Liver Fibrosis in Patients with Chronic Hepatitis B or C by Contrast-Enhanced Magnetic Resonance Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
René Hako ◽  
Pavol Kristian ◽  
Peter Jarčuška ◽  
Ivana Haková ◽  
Ivana Hockicková ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Liver Fibrosis ◽  
Liver Biopsy ◽  
Control Group ◽  
Dce Mri ◽  
Triple Test ◽  
Severe Stage ◽  
Contrast Enhanced ◽  
Liver Surface ◽  
Surface Changes

Background and Aim. To develop a noninvasive magnetic resonance imaging (MRI) method for evaluation of liver fibrosis. We evaluate the utility of hepatocyte-phase Gadoxetate disodium–enhanced magnetic resonance (MR) imaging in staging hepatic fibrosis and compare it with histological analysis as the reference standard (liver biopsy). Methods. Prospective cohort of 78 patients, who received Gadoxetate disodium dynamic contrast-enhanced MRI (DCE-MRI), were divided into three groups. The first group (n=19) was a control group of healthy individuals without liver injury and remaining 59 subjects were chronic hepatitis B and C patients who underwent liver biopsy. These patients were divided into the mild fibrosis F1-F2 (n=32) and advanced fibrosis F3-F4 (n=27) groups. Patients were examined by generated DCE-MRI in 20th minute. Variables such as liver surface changes, homogeneities, and quantitative contrast liver/spleen ratio-Q-LSCR were evaluated and these results were consequently compared between the three groups. Results. Gd-EOB-DTPA contrast-enhanced dynamic liver MRI examination (DCE-MRI) can in the 20th minute differentiate mild stage of liver fibrosis (F1-F2) from severe stage of liver fibrosis (F3-F4) on the basis of liver surface changes, homogeneities, and quantitative contrast liver/spleen ratio-Q-LSCR. Diagnostic MRI criteria were created and named MRI Triple test. This test correctly identified 96% of patients with F3-F4 fibrosis and 91% of patients with the F1-F2 fibrosis in the liver biopsy. This test correctly identified 42,1% of patients in the control group (presumed F0 fibrosis without liver disease). Spearman's rank correlation coefficient (r = 0,86, P < .001) confirmed high agreement of biopsy and MR Triple test. MR Triple test’s sensitivity was 96.30% (95%CI 81.03% to 99.91%), specificity 90.62% (95%CI 74.98% to 98.02%), positive predictive value 89.66% (95%CI 74.64% to 96.23%), and negative predictive value 96.67% (95%CI 80.86% to 99.50%) for discrimination between F3-4 and F1-2 fibrosis on liver biopsy. Conclusions. Gd-EOB-DTPA contrast-enhanced MRI liver examination in 20th minute is able to reliably differentiate mild stage of liver fibrosis (F1-F2) from severe stage fibrosis (F3-F4) on the basis of Triple test (liver surface changes, homogeneities, and quantitative contrast liver/spleen ratio-Q-LSCR).

scholarly journals MAGNETIC RESONANCE IMAGING PERFUSION TECHNIQUE IN THE EVALUATION OF ACHILLES TENDON INJURY IN RABBITS

2019 ◽  
Vol 27 (1) ◽  
pp. 12-15
Author(s):  
Wilson Campos Tavares Junior ◽  
Eduardo Paulino ◽  
Maria Angélica Baron Magalhaes ◽  
Ana Clara Guimarães Gabrich Fonseca ◽  
João Bernardo Sancio Rocha Rodrigues ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Achilles Tendon ◽  
White Male ◽  
Tendon Injury ◽  
Control Group ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

ABSTRACT Objective: This study aimed to evaluate the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the experimental model of Achilles tendon injury. Methods: Twelve white male adults New Zealand rabbits were divided into two groups, a group with resection of the central portion of the Achilles tendon (n = 8) and a control group (n = 4). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed 4 weeks after the surgical procedure, followed by histological analysis of the tendons. Results: The main finding of this study was the difference (p < 0.001) in peak contrast enhancement on DCE-MRI, which demonstrated that the operated group had greater contrast uptake. The operated tendons showed histological disruption of their architecture, and cluttered appearance of tendinous fibers, with vascular and fibroblast proliferations. Conclusion: DCE-MRI is a technique with a potential to demonstrate changes in the vascularity pattern of the Achilles tendon before and after operation. DCE-MRI has a potential to be used in studies of tendinosis diagnosis and surgical follow-up. Level of evidence II, Experimental Study.

Dynamic contrast-enhanced magnetic resonance imaging for monitoring the anti-angiogenesis efficacy in a C6 glioma rat model

2019 ◽  
Vol 61 (7) ◽  
pp. 973-982 ◽  
Author(s):  
Weishu Hou ◽  
Xiaohu Li ◽  
Hongli Pan ◽  
Man Xu ◽  
Sixing Bi ◽  
...  
Keyword(s):  
Tumor Volume ◽  
Treated Group ◽  
C6 Glioma ◽  
Control Group ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Angiogenic Therapy ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Early Effects

Background Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is useful in predicting responses to angiogenic therapy of malignant tumors. Purpose To observe the dynamics of DCE-MRI parameters in evaluating early effects of antiangiogenic therapy in a C6 glioma rat model. Material and Methods The Bevacizumab or vehicle treatment was started from the 14th day after glioma model was established. The treated and control groups (n = 13 per group) underwent DCE-MRI scans on days 0, 1, 3, 5, and 7 after treatment. Tumor volume was calculated according to T2-weighted images. Hematoxylin and eosin, microvessel density (MVD), and proliferating cell nuclear antigen (PCNA) examination were performed on day 7. The MRI parameters between the two groups were compared and correlations with immunohistochemical scores were analyzed. Results The average tumor volume of treated group was significantly lower than that of control group on day 7 (81.764 ± 1.043 vs. 103.634 ± 3.868 mm3, P =  0.002). Ktrans and Kep decreased in the treated group while they increased in the control group. The differences were observed on day 5 (Ktrans: 0.045 ± 0.018 vs. 0.093 ± 0.014 min−1, P <  0.001; Kep: 0.062 ± 0.018 vs. 0.134 ± 0.047 min−1, P =  0.005) and day 7 (Ktrans: 0.032 ± 0.010 vs. 0.115 ± 0.025 min−1, P <  0.001; Kep: 0.045 ± 0.016 vs. 0.144 ± 0.042 min−1, P <  0.001). The difference of Ve was observed on day 5 (0.847 ± 0.248 vs. 0.397 ± 0.151, P =  0.009) and 7 (0.920 ± 0.154 vs. 0.364 ± 0.105, P =  0.006). Ktrans and Kep showed positive correlations with MVD and Ve showed negative correlation with PCNA. Conclusion DCE-MRI can assess the changes of early effects of anti-angiogenic therapy in preclinical practice.

scholarly journals Liver fibrosis promotes immunity escape but limits the size of liver tumor in a rat orthotopic transplantation model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tongqiang Li ◽  
Jiacheng Liu ◽  
Yingliang Wang ◽  
Chen Zhou ◽  
Qin Shi ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Liver Cancer ◽  
Liver Tumor ◽  
Tumor Size ◽  
Immune Escape ◽  
Normal Liver ◽  
Control Group ◽  
Interesting Phenomenon ◽  
Tumor Immune Escape ◽  
Fibrotic Liver

AbstractLiver fibrosis plays a crucial role in promoting tumor immune escape and tumor aggressiveness for liver cancer. However, an interesting phenomenon is that the tumor size of liver cancer patients with liver fibrosis is smaller than that of patients without liver fibrosis. In this study, 16 SD rats were used to establish orthotopic liver tumor transplantation models with Walker-256 cell lines, respectively on the fibrotic liver (n = 8, LF group) and normal liver (n = 8, control group). MRI (magnetic resonance imaging) was used to monitor the size of the tumors. All rats were executed at the third week after modeling, and the immunohistochemical staining was used to reflect the changes in the tumor microenvironment. The results showed that, compared to the control group, the PD-L1 (programmed cell death protein receptor-L1) expression was higher, and the neutrophil infiltration increased while the effector (CD8+) T cell infiltration decreased in the LF group. Additionally, the expression of MMP-9 (matrix metalloproteinase-9) of tumor tissue in the LF group increased. Three weeks after modeling, the size of tumors in the LF group was significantly smaller than that in the control group (382.47 ± 195.06 mm3 vs. 1736.21 ± 657.25 mm3, P < 0.001). Taken together, we concluded that liver fibrosis facilitated tumor immunity escape but limited the expansion of tumor size.

scholarly journals Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

2021 ◽  
Vol 11 (4) ◽  
pp. 1880
Author(s):  
Roberta Fusco ◽  
Adele Piccirillo ◽  
Mario Sansone ◽  
Vincenza Granata ◽  
Paolo Vallone ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Feature Selection ◽  
Resonance Imaging ◽  
Dynamic Features ◽  
Surgical Specimen ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Malignant Lesions

Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were analyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions.

scholarly journals Can DWI-MRI be an alternative to DCE-MRI in the diagnosis of troublesome breast lesions?

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Lamiaa Mohamed Bassam Hashem ◽  
Sherihan W. Y. Gareer ◽  
Aya Mohamed Bassam Hashem ◽  
Sherihan Fakhry ◽  
Yasmin Mounir Tohamey
Keyword(s):  
Problem Solver ◽  
Breast Lesions ◽  
Diffusion Weighted Mri ◽  
Resonance Imaging ◽  
Contrast Administration ◽  
Dce Mri ◽  
Water Diffusivity ◽  
Contrast Enhanced ◽  
Mri Study ◽  
Established Technique

Abstract Background Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has always been a problem solver in troublesome breast lesions. Despite its many advantages, the encountered low specificity results in unnecessary biopsies. Diffusion-weighted MRI (DW-MRI) is a well-established technique that helps in characterizing breast lesions according to their water diffusivity. So this work aimed to assess the diagnostic performance of DW-MRI in troublesome breast lesions and see if it can replace DCE-MRI study. Results In our prospective study, we included 86 patients with mammography and/or ultrasound-detected 90 probably benign or probably malignant (BIRADS 3 or 4) breast lesions. Among the studied cases, 49/90 lesions were benign, and 41/90 were malignant. Combined analysis of morphological and kinetic findings in DCE-MRI had achieved the highest sensitivity of 95.1%. DW-MRI alone was less sensitive (73.2%) yet more specific (83.7%) than DCE-MRI (77.6%). Diagnostic accuracy of DCE-MRI was higher (85.6%) as compared to DW-MRI which was (78.9%). Conclusion DCE-MRI is the cornerstone in the workup of troublesome breast lesions. DW-MRI should not be used as supplementary tool unless contrast administration is contraindicated. Combining both DCE-MRI and DW-MRI is the ultimate technique for better lesion evaluation.

scholarly journals Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model

2021 ◽  
Author(s):  
Yunchao Yin ◽  
Derya Yakar ◽  
Rudi A. J. O. Dierckx ◽  
Kim B. Mouridsen ◽  
Thomas C. Kwee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Fibrosis ◽  
Liver Parenchyma ◽  
Ct Images ◽  
Fibrosis Stage ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Liver Surface ◽  
Fibrosis Staging

Abstract Objectives Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. Methods The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. Results The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2–F4), advanced fibrosis (F3–F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). Conclusions Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning–based liver fibrosis staging algorithms. Key Points • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage.

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