We evaluated the performance of intravoxel incoherent motion (IVIM) parameters for preoperatively predicting the subtype and Masaoka stage of thymic epithelial tumors (TETs). Seventy-seven patients with pathologically confirmed TETs underwent a diffusion weighted imaging (DWI) sequence with 9 b values. Differences in the slow diffusion coefficient (D), fast perfusion coefficient (D*), and perfusion fraction (f) IVIM parameters, as well as the multi b-value fitted apparent diffusion coefficient (ADCmb), were compared among patients with low-risk (LRT) and high-risk thymomas (HRT) and thymic carcinomas (TC), and between early stage (stages I and II) and advanced stage (stages III and IV) TET patients. ADCmb, D, and D* values were higher in the LRT group than in the HRT or TC group, but did not differ between the HRT and TC groups. The mean ADCmb, D, and D* values were higher in the early stage TETs group than the advanced stage TETs group. The f values did not differ among the groups. These results suggest that IVIM DWI could be used to preoperatively predict subtype and Masaoka stage in TET patients.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5546503 | PMC |
| http://dx.doi.org/10.18632/oncotarget.17857 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI.
Med Image Anal
November 2024
Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge lies in accurately estimating model parameters from the acquired data due to the inherently low signal-to-noise ratio (SNR) of the signal measurements and the complexity of solving the ill-posed inverse problem. Conventional model fitting approaches treat individual voxels as independent.
View Article and Find Full Text PDFNomogram based on the neutrophil-to-lymphocyte ratio and MR diffusion quantitative parameters for predicting Ki67 expression in hepatocellular carcinoma from a prospective study.
Sci Rep
December 2024
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
This study aimed to establish and validate a multiparameter prediction model for Ki67 expression in hepatocellular carcinoma (HCC) patients while also exploring its potential to predict the one-year recurrence risk. The clinical, pathological, and imaging data of 83 patients with HCC confirmed by postoperative pathology were analyzed, and the patients were randomly divided into a training set (n = 58) and a validation set (n = 25) at a ratio of 7:3. All patients underwent a magnetic resonance imaging (MRI) scan that included multi-b value diffusion-weighted scanning before surgery, and quantitative parameters were obtained via intravoxel incoherent motion (IVIM) and diffusion kurtosis (DKI) models.
View Article and Find Full Text PDFPerfusion Showdown: Comparison of Multiple MRI Perfusion Techniques in the Grading of Pediatric Brain Tumors.
AJNR Am J Neuroradiol
December 2024
From the Department of Diagnostic Medicine, Dell Medical School at The University of Texas at Austin, Austin, TX, USA (C.Y.H.), Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA (N.S., G.A., Q.W., P.C., M.A., J.G.P., B.R.G., P.R.T., G.D.H.), Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA (E.C., P.R.T., S.A.P.), Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA (P.R.T., S.A.P.), and the Department of Radiology at Texas Children's Hospital, Houston, TX, USA (S.F.K.).
Background And Purpose: There are multiple MRI perfusion techniques, with limited available literature comparing these techniques in the grading of pediatric brain tumors. For efficiency and limiting scan time, ideally only one MRI perfusion technique can be used in initial imaging. We compared DSC, DCE, and IVIM along with ADC from DWI for differentiating high versus low grade pediatric brain tumors.
View Article and Find Full Text PDFMonoexponential, biexponential, stretched exponential and diffusion kurtosis models of diffusion-weighted imaging: a quantitative differentiation of solitary pulmonary lesion.
BMC Med Imaging
December 2024
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Background: Diffusion-weighted imaging (DWI) can be used for quantitative tumor assessment. DWI with different models may show different aspects of tissue characteristics.
Objective: To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, stretched exponential magnetic resonance diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant solitary pulmonary lesions (SPLs).
Improved deep learning-based IVIM parameter estimation via the use of more "realistic" simulated brain data.
Med Phys
December 2024
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian, China.
Background: Due to the low signal-to-noise ratio (SNR) and the limited number of b-values, precise parameter estimation of intravoxel incoherent motion (IVIM) imaging remains an open issue to date, especially for brain imaging where the relatively small difference between D and D easily leads to outliers and obvious graininess in estimated results.
Purpose: To propose a synthetic data driven supervised learning method (SDD-IVIM) for improving precision and noise robustness in IVIM parameter estimation without relying on real-world data for neural network training.
Methods: On account of the absence of standard IVIM parametric maps from real-world data, a novel model-based method for generating synthetic human brain IVIM data was introduced.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!