Intravoxel incoherent motion diffusion-weighted MR imaging parameters predict pathological classification in thymic epithelial tumors

Li, Gangfeng; Duan, Shijun; Yan, Lin‐Feng; Wang, Wen; Jing, Yong; Wang, Yan; Sun, Qian; Wang, Shumei; Nan, Hai-Yan; Xu, Tian-Yong; Zheng, Dandan; Hu, Yu-Chuan; Cui, Guangbin

doi:10.18632/oncotarget.17857

Cited by 11 publications

(6 citation statements)

References 31 publications

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“… 20 Similarly, the maximal contrast-enhanced range from contrast-enhanced CT, blood volume value, and permeability from perfusion CT, and the fast diffusion coefficient (D*) value from intravoxel incoherent motion diffusion-weighted imaging in low-risk thymomas were significantly higher than that in high-risk thymomas or thymic carcinomas. 5 , 9 , 33 This interesting blood flow characteristic of TETs can be explained by a pathologic research, which demonstrated that the short-spindled variant (57% histologic patterns of thymoma type A and AB) was composed of oval to short spindle cells commonly arranged in a hemangiopericytic or microcystic pattern. 34 Therefore, taken together, these results suggest that DECT parameters might be valuable for differentiating thymic tumors.…”

Section: Discussionmentioning

confidence: 97%

Iodine Quantification Using Dual-Energy Computed Tomography for Differentiating Thymic Tumors

Wang¹,

Xin²,

Jing³

et al. 2018

J Comput Assist Tomogr

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PurposeThe aim of the study was to explore the efficacy of iodine quantification with dual-energy computed tomography (DECT) in differentiating thymoma, thymic carcinoma, and thymic lymphoma.Materials and MethodsFifty-seven patients with pathologically confirmed low-risk thymoma (n = 16), high-risk thymoma (n = 15), thymic carcinoma (n = 14), and thymic lymphoma (n = 12) underwent chest contrast-enhanced DECT scan were enrolled in this study. Tumor DECT parameters including iodine-related Hounsfield unit (IHU), iodine concentration (IC), mixed HU (MHU), and iodine ratio in dual phase, slope of energy spectral HU curve (λ), and virtual noncontrast (VNC) were compared for differences among 4 groups by one-way analysis of variance. Receiver operating characteristic curve was used to determine the efficacy for differentiating the low-risk thymoma from other thymic tumor by defined parameters.ResultsAccording to quantitative analysis, dual-phase IHU, IC, and MHU values in patients with low-risk thymoma were significantly increased compared with patients with high-risk thymoma, thymic carcinoma, and thymic lymphoma (P < 0.05/4).The venous phase IHU value yielded the highest performance with area under the curve of 0.893, 75.0% sensitivity, and 89.7% specificity for differentiating the low-risk thymomas from high-risk thymomas or thymic carcinoma at the cutoff value of 34.3 HU. When differentiating low-risk thymomas from thymic lymphoma, the venous phase IC value obtained the highest diagnostic efficacy with the area under the curve of 0.969, and sensitivity, specificity, and cutoff value were 87.5%, 100.0%, and 1.25 mg/mL, respectively.ConclusionsIodine quantification with DECT may be useful for differentiating the low-risk thymomas from other thymic tumors.

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Section: Discussionmentioning

confidence: 97%

Iodine Quantification Using Dual-Energy Computed Tomography for Differentiating Thymic Tumors

Wang¹,

Xin²,

Jing³

et al. 2018

J Comput Assist Tomogr

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“…(1) can then be simplified, and the estimation of D can be obtained by using only b values greater than 200 s/mm 2 with a simple linear fit eq. (2) [22,43]:…”

Section: Ivim Dwi Data Processingmentioning

confidence: 99%

Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain?

Yan

Han

et al. 2020

BMC Med Imaging

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Background: Our study aims to reveal whether the low b-values distribution, high b-values upper limit, and the number of excitation (NEX) influence the accuracy of the intravoxel incoherent motion (IVIM) parameter derived from multi-b-value diffusion-weighted imaging (DWI) in the brain. Methods: This prospective study was approved by the local Ethics Committee and informed consent was obtained from each participant. The five consecutive multi-b DWI with different b-value protocols (0-3500 s/mm 2 ) were performed in 22 male healthy volunteers on a 3.0-T MRI system. The IVIM parameters from normal white matter (WM) and gray matter (GM) including slow diffusion coefficient (D), fast perfusion coefficient (D*) and perfusion fraction (f) were compared for differences among defined groups with different IVIM protocols by one-way ANOVA. Results: The D* and f value of WM or GM in groups with less low b-values distribution (less than or equal to 5 b-values) were significantly lower than ones in any other group with more low b-values distribution (all P < 0.05), but no significant differences among groups with more low b-values distribution (P > 0.05). In addition, no significant differences in the D, D* and f value of WM or GM were found between group with one and more NEX of low b-values distribution (all P > 0.05). IVIM parameters in normal WM and GM strongly depended on the choice of the high b-value upper limit. Conclusions: Metrics of IVIM parameters can be affected by low and high b value distribution. Eight low b-values distribution with high b-value upper limit of 800-1000 s/mm 2 may be the relatively proper set when performing brain IVIM studies.

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“…Diffusion weighted imaging (DWI) is a noninvasive approach to probe molecular diffusion of water without contrast administration, and the diffusion of water can be quantitatively described by apparent diffusion coefficient (ADC) [ 16 , 17 ]. However, the ADC value derived from DWI is calculated by using the mono-exponential model, which ignored the effect of perfusion fraction in tissue and could be influenced by the microcirculation of blood in capillaries [ 18 , 19 ]. Intravoxel incoherent motion (IVIM) diffusion-weighted MR imaging could determine the true molecular diffusion and perfusion from the blood microcirculation in the capillary networks by using multi b values, and thus has the potential to better characterize tissue than DWI [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Intrahepatic cholangiocarcinoma in the setting of HBV-related cirrhosis: Differentiation with hepatocellular carcinoma by using Intravoxel incoherent motion diffusion-weighted MR imaging

Gao

Zheng³

et al. 2017

Oncotarget

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Accurate preoperative differentiation of intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) in the setting of cirrhotic liver is of great clinical significance because the treatment and prognosis of these entities differ markedly. Through a retrospectively research, we sought to determine the diagnostic performances of intravoxel incoherent motion (IVIM) and diffusion weighted imaging (DWI) parameters in the differentiating of ICC and HCC. According to the results, we found that apparent diffusion coefficient (ADC) derived from mono-exponential model and true ADC (ADCslow) derived from bi-exponential model can be used to distinguish the ICC and HCC, and ADCslowentailed the higher diagnostic performance than ADC. However, pseudo-ADC (ADCfast) and perfusion fraction (f) can not be used to differentiate ICC and HCC. These results suggested that IVIM and DWI parameters can be useful in differentiating ICC and HCC and might be helpful in selecting the treatment plan and predicting prognosis.

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Intravoxel incoherent motion diffusion-weighted MR imaging parameters predict pathological classification in thymic epithelial tumors

Cited by 11 publications

References 31 publications

Iodine Quantification Using Dual-Energy Computed Tomography for Differentiating Thymic Tumors

Iodine Quantification Using Dual-Energy Computed Tomography for Differentiating Thymic Tumors

Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain?

Intrahepatic cholangiocarcinoma in the setting of HBV-related cirrhosis: Differentiation with hepatocellular carcinoma by using Intravoxel incoherent motion diffusion-weighted MR imaging

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