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Yamashita K, Hatae R, Kikuchi K, Kuga D, Hata N, Yamamoto H, Obara M, Yoshimoto K, Ishigami K, Togao O. Predicting TERT promoter mutation status using 1H-MR spectroscopy and stretched-exponential model of diffusion-weighted imaging in IDH-wildtype diffuse astrocytic glioma without intense enhancement. Neuroradiology 2023:10.1007/s00234-023-03177-y. [PMID: 37308686 DOI: 10.1007/s00234-023-03177-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/04/2023] [Indexed: 06/14/2023]
Abstract
PURPOSE Isocitrate dehydrogenase (IDH)-wildtype diffuse astrocytic glioma with telomerase reverse transcriptase (TERT) promoter mutation is defined as glioblastoma by the WHO 2021 criteria, revealing that TERT promotor mutation is highly associated with tumor aggressiveness. The aim of this study was to identify features from MR spectroscopy (MRS) and multi-exponential models of DWI distinguishing wild-type TERT (TERTw) from TERT promoter mutation (TERTm) in IDH-wildtype diffuse astrocytic glioma. METHODS Participants comprised 25 adult patients with IDH-wildtype diffuse astrocytic glioma. Participants were classified into TERTw and TERTm groups. Point-resolved spectroscopy sequences were used for MRS data acquisition. DWI was performed with 13 different b-factors. Peak height ratios of NAA/Cr and Cho/Cr were calculated from MRS data. Mean apparent diffusion coefficient (ADC), perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient (D*), distributed diffusion coefficient (DDC), and heterogeneity index (α) were obtained using multi-exponential models from DWI data. Each parameter was compared between TERTw and TERTm using the Mann-Whitney U test. Correlations between parameters derived from MRS and DWI were also evaluated. RESULTS NAA/Cr and Cho/Cr were both higher for TERTw than for TERTm. The α of TERTw was smaller than that of TERTm, while the f of TERTw was higher than that of TERTm. NAA/Cr correlated negatively with α, but not with other DWI parameters. Cho/Cr did not show significant correlations with any DWI parameters. CONCLUSION The combination of NAA/Cr and α may have merit in clinical situation to predict the TERT mutation status of IDH-wildtype diffuse astrocytic glioma without intense enhancement.
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Affiliation(s)
- Koji Yamashita
- Departments of Radiology Informatics and Network, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
| | - Ryusuke Hatae
- Departments of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Kazufumi Kikuchi
- Departments of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Daisuke Kuga
- Departments of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Nobuhiro Hata
- Departments of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Hidetaka Yamamoto
- Departments of Anatomic Pathology Pathologic Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Makoto Obara
- Philips Japan, 13-37, Kohnan 2-Chome, Minato-Ku, Tokyo, 108-8507, Japan
| | - Koji Yoshimoto
- Departments of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Kousei Ishigami
- Departments of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Osamu Togao
- Departments of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
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Wang J, Zhang H, Dang X, Rui W, Cheng H, Wang J, Zhang Y, Qiu T, Yao Z, Liu H, Pang H, Ren Y. Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study. Front Oncol 2023; 13:1104610. [PMID: 37182187 PMCID: PMC10171458 DOI: 10.3389/fonc.2023.1104610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 04/13/2023] [Indexed: 05/16/2023] Open
Abstract
Background To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas. Methods Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC1500, α1500 fitted by 15 b-values (0-1,500 sec/mm2)and DDC5000 and α5000 fitted by 22 b-values (0-5,000 sec/mm2) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters. Results MDWI-derived α1500 negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC1500 and DDC5000 negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α1500(r=-0.485; P=0.005) and α5000(r=-0.395; P=0.025). Conclusions SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.
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Affiliation(s)
- Junlong Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hua Zhang
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Xuefei Dang
- Department of Oncology, Minhang Branch of Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wenting Rui
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Haixia Cheng
- Department of Neuropathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yong Zhang
- Department of Magnetic Resonance Research, General Electric Healthcare, Shanghai, China
| | - Tianming Qiu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hanqiu Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Hanqiu Liu, ; Haopeng Pang, ; Yan Ren,
| | - Haopeng Pang
- Minimally Invasive Therapy Center, Shanghai Cancer Center, Fudan University, Shanghai, China
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Hanqiu Liu, ; Haopeng Pang, ; Yan Ren,
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Hanqiu Liu, ; Haopeng Pang, ; Yan Ren,
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Luo Y, Jiang H, Meng N, Huang Z, Li Z, Feng P, Fang T, Fu F, Yuan J, Wang Z, Yang Y, Wang M. A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types. Front Oncol 2022; 12:907860. [PMID: 35936757 PMCID: PMC9351313 DOI: 10.3389/fonc.2022.907860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/28/2022] [Indexed: 11/24/2022] Open
Abstract
Objective To evaluate the application value of monoexponential, fractional order calculus (FROC) diffusion models and PET imaging to distinguish between benign and malignant solitary pulmonary lesions (SPLs) and malignant SPLs with different pathological types and explore the correlation between each parameter and Ki67 expression. Methods A total of 112 patients were enrolled in this study. Prior to treatment, all patients underwent a dedicated thoracic 18F-FDG PET/MR examination. Five parameters [including apparent diffusion coefficient (ADC) derived from the monoexponential model; diffusion coefficient (D), a microstructural quantity (μ), and fractional order parameter (β) derived from the FROC model and maximum standardized uptake value (SUVmax) derived from PET] were compared between benign and malignant SPLs and different pathological types of malignant SPLs. Independent sample t test, Mann-Whitney U test, DeLong test and receiver operating characteristic (ROC) curve analysis were used for statistical evaluation. Pearson correlation analysis was used to calculate the correlations between Ki-67 and ADC, D, μ, β, and SUVmax. Results The ADC and D values were significantly higher and the μ and SUVmax values were significantly lower in the benign group [1.57 (1.37, 2.05) μm2/ms, 1.59 (1.52, 1.72) μm2/ms, 5.06 (3.76, 5.66) μm, 5.15 ± 2.60] than in the malignant group [1.32 (1.03, 1.51) μm2/ms, 1.43 (1.29, 1.52) μm2/ms, 7.06 (5.87, 9.45) μm, 9.85 ± 4.95]. The ADC, D and β values were significantly lower and the μ and SUVmax values were significantly higher in the squamous cell carcinoma (SCC) group [1.29 (0.66, 1.42) μm2/ms, 1.32 (1.02, 1.42) μm2/ms, 0.63 ± 0.10, 9.40 (7.76, 15.38) μm, 11.70 ± 5.98] than in the adenocarcinoma (AC) group [1.40 (1.28, 1.67) μm2/ms, 1.52 (1.44, 1.64) μm2/ms, 0.70 ± 0.10, 5.99 (4.54, 6.87) μm, 8.76 ± 4.18]. ROC curve analysis showed that for a single parameter, μ exhibited the best AUC value in discriminating between benign and malignant SPLs groups and AC and SCC groups (AUC = 0.824 and 0.911, respectively). Importantly, the combination of monoexponential, FROC models and PET imaging can further improve diagnostic performance (AUC = 0.872 and 0.922, respectively). The Pearson correlation analysis showed that Ki67 was positively correlated with μ value and negatively correlated with ADC and D values (r = 0.402, -0.346, -0.450, respectively). Conclusion The parameters D and μ derived from the FROC model were superior to ADC and SUVmax in distinguishing benign from malignant SPLs and adenocarcinoma from squamous cell carcinoma, in addition, the combination of multiple parameters can further improve diagnostic performance. The non-Gaussian FROC diffusion model is expected to become a noninvasive quantitative imaging technique for identifying SPLs.
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Affiliation(s)
- Yu Luo
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Han Jiang
- Department of Medical Imaging, Xinxiang Medical University & Henan Provincial People’s Hospital, Xinxiang, Henan, China
| | - Nan Meng
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhun Huang
- Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Ziqiang Li
- Department of Medical Imaging, Xinxiang Medical University & Henan Provincial People’s Hospital, Xinxiang, Henan, China
| | - Pengyang Feng
- Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Ting Fang
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Fangfang Fu
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
- *Correspondence: Meiyun Wang,
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Chaudhary N, Zhang G, Li S, Zhu W. Monoexponential, biexponential and stretched exponential models of diffusion weighted magnetic resonance imaging in glioma in relation to histopathologic grade and Ki-67 labeling index using high B values. Am J Transl Res 2021; 13:12480-12494. [PMID: 34956467 PMCID: PMC8661204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 03/12/2021] [Indexed: 06/14/2023]
Abstract
PURPOSE To explore the performance of various parameters obtained from monoexponential (Gaussian), biexponential and stretched exponential (non-Gaussian) models of Diffusion Weighted Magnetic Resonance Imaging in differentiating gliomas with correlation to histopathology and Ki-67 labeling index (LI). MATERIALS AND METHODS This Institute Review Board approved retrospective study included 51 pathologically proven glioma patients (WHO Grade I, n = 1; Grade II, n = 19, Grade III, n = 12; Grade IV, n = 19), and immunohistochemistry for Ki-67 LI was obtained. The conventional Magnetic Resonance (MR) images and Diffusion Weighted (DW) images with 19 non-zero b values (0-4500 s/mm2) followed by contrast-enhanced MR images were obtained at 3T preoperatively. All images were processed with Advantage Workstation 4.5 (GE Medical Systems). Region of interest (ROI) in the solid part of the tumor was manually drawn along the border meticulously excluding areas of edema, cyst, hemorrhage, necrosis, and/or calcification, and the parameters: Apparent Diffusion Coefficient (ADC) of monoexponential; pure molecular diffusion coefficient (Dslow), pseudo-diffusion coefficient (Dfast), and perfusion fraction (f) of biexponential; Distributed Diffusion Coefficient (DDC), and heterogeneity index (α) of stretched exponential models were obtained. ROI of 50 mm2 in the contralateral normal appearing white matter (NAWM) was drawn for the internal control either on centrum semiovale or white matter of the frontal lobe. Analysis of reliability by Intra-class Correlation Coefficient (ICC); correlation with Ki-67 LI by Spearman's rank correlation; comparison between high grade glioma (HGG) and low grade glioma (LGG) by either Mann Whitney U test or Independent t-Test; comparison among Grade II, III and IV gliomas by one-way ANOVA with Bonferroni; and diagnostic performance by analysis of Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were conducted. RESULTS Highly significant differences were found between HGG and LGG for all the parameters (P < 0.001 for all). In differentiating HGG from LGG, AUC values were 0.955 for Ki-67 LI; 0.926 for α; 0.903 for Dslow; 0.897 for f; 0.863 for DDC; 0.852 for ADC; 0.820 for Dfast (P < 0.001 for all). The parameters ADC, Dslow, Dfast, f, DDC, and α showed moderate to good negative correlation with Ki-67 LI (P < 0.001 for all). The ICCs of all the parameters were found greater than 0.75 (P < 0.05 for all) suggesting good reliability of measurements. CONCLUSION In comparison to ADC derived from monoexponential model, the parameters α and Dslow derived from stretched exponential, and biexponential models respectively can efficiently differentiate HGG from LGG with high diagnostic accuracy. Additionally, f and DDC derived from biexponential, and stretched exponential models respectively are also more useful in differentiating HGG from LGG in comparison to ADC.
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Affiliation(s)
- Nabin Chaudhary
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology No. 1095 Jiefang Avenue, Wuhan 430030, Hubei, China
| | - Guiling Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology No. 1095 Jiefang Avenue, Wuhan 430030, Hubei, China
| | - Shihui Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology No. 1095 Jiefang Avenue, Wuhan 430030, Hubei, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology No. 1095 Jiefang Avenue, Wuhan 430030, Hubei, China
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Papoutsaki MV, Sidhu HS, Dikaios N, Singh S, Atkinson D, Kanber B, Beale T, Morley S, Forster M, Carnell D, Mendes R, Punwani S. Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers. NMR IN BIOMEDICINE 2021; 34:e4587. [PMID: 34240782 DOI: 10.1002/nbm.4587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 06/13/2023]
Abstract
Diffusion MRI characteristics assessed by apparent diffusion coefficient (ADC) histogram analysis in head and neck squamous cell carcinoma (HNSCC) have been reported as helpful in classifying tumours based on diffusion characteristics. There is little reported on HNSCC lymph nodes classification by diffusion characteristics. The aim of this study was to determine whether pretreatment nodal microstructural diffusion MRI characteristics can classify diseased nodes of patients with HNSCC from normal nodes of healthy volunteers. Seventy-nine patients with histologically confirmed HNSCC prior to chemoradiotherapy, and eight healthy volunteers, underwent diffusion-weighted (DW) MRI at a 1.5-T MR scanner. Two radiologists contoured lymph nodes on DW (b = 300 s/m2 ) images. ADC, distributed diffusion coefficient (DDC) and alpha (α) values were calculated by monoexponential and stretched exponential models. Histogram analysis metrics of drawn volume were compared between patients and volunteers using a Mann-Whitney test. The classification performance of each metric between the normal and diseased nodes was determined by receiver operating characteristic (ROC) analysis. Intraclass correlation coefficients determined interobserver reproducibility of each metric based on differently drawn ROIs by two radiologists. Sixty cancerous and 40 normal nodes were analysed. ADC histogram analysis revealed significant differences between patients and volunteers (p ≤0.0001 to 0.0046), presenting ADC distributions that were more skewed (1.49 for patients, 1.03 for volunteers; p = 0.0114) and 'peaked' (6.82 for patients, 4.20 for volunteers; p = 0.0021) in patients. Maximum ADC values exhibited the highest area under the curve ([AUC] 0.892). Significant differences were revealed between patients and volunteers for DDC and α value histogram metrics (p ≤0.0001 to 0.0044); the highest AUC were exhibited by maximum DDC (0.772) and the 25th percentile α value (0.761). Interobserver repeatability was excellent for mean ADC (ICC = 0.88) and the 25th percentile α value (ICC = 0.78), but poor for all other metrics. These results suggest that pretreatment microstructural diffusion MRI characteristics in lymph nodes, assessed by ADC and α value histogram analysis, can identify nodal disease.
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Affiliation(s)
| | | | - Nikolaos Dikaios
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
| | - Saurabh Singh
- Centre for Medical Imaging, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - Baris Kanber
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Timothy Beale
- Department of Radiology, University College London Hospital, London, UK
| | - Simon Morley
- Department of Radiology, University College London Hospital, London, UK
| | - Martin Forster
- Department of Oncology, University College London, Cancer Institute, London, UK
- Department of Oncology, University College London Hospital, London, UK
| | - Dawn Carnell
- Department of Oncology, University College London Hospital, London, UK
| | - Ruheena Mendes
- Department of Oncology, University College London Hospital, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
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Li Y, Kim MM, Wahl DR, Lawrence TS, Parmar H, Cao Y. Survival Prediction Analysis in Glioblastoma With Diffusion Kurtosis Imaging. Front Oncol 2021; 11:690036. [PMID: 34336676 PMCID: PMC8316991 DOI: 10.3389/fonc.2021.690036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/24/2021] [Indexed: 11/13/2022] Open
Abstract
SIMPLE SUMMARY Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Diffusion kurtosis imaging (DKI) has characterized non-Gaussian diffusion behaviors in brain normal tissue and gliomas, but there are very limited efforts in investigating treatment responses of kurtosis in GBM. This study aimed to investigate whether any parameter derived from the DKI is a significant predictor of overall survival (OS). We found that the large mean, 80 and 90 percentile kurtosis values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1-weighted images pre-RT were significantly associated with reduced OS. In the multivariate Cox model, the mean kurtosis Gd-GTV pre-RT after considering effects of age, extent of surgery, and methylation were significant predictors of OS. In addition, the 80 and 90 percentile kurtosis values in Gd-GTV post RT were significantly associated with progression free survival (PFS). The DKI model demonstrates the potential to predict outcomes in the patients with GBM. PURPOSE Non-Gaussian diffusion behaviors in gliomas have been characterized by diffusion kurtosis imaging (DKI). But there are very limited efforts in investigating the kurtosis in glioblastoma (GBM) and its prognostic and predictive values. This study aimed to investigate whether any of the diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival. METHODS AND MATERIALS Thirty-three patients with GBM had pre-radiation therapy (RT) and mid-RT diffusion weighted (DW) images. Kurtosis and diffusion coefficient (DC) values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1 weighted images pre-RT and mid-RT were calculated. Univariate and multivariate Cox models were used to evaluate the DKI parameters and clinical factors for prediction of OS and PFS. RESULTS The large mean kurtosis values in the Gd-GTV pre-RT were significantly associated with reduced OS (p = 0.02), but the values at mid-RT were not (p > 0.8). In the multivariate Cox model, the mean kurtosis in the Gd-GTV pre-RT (p = 0.009) was still a significant predictor of OS after adjusting effects of age, O6-Methylguanine-DNA Methyl transferase (MGMT) methylation and extent of resection. In Gd-GTV post-RT, 80 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS). CONCLUSION The DKI model demonstrates the potential to predict OS and PFS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.
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Affiliation(s)
- Yuan Li
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Michelle M. Kim
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Daniel R. Wahl
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Theodore S. Lawrence
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Hemant Parmar
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Yue Cao
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
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Momeni F, Abedi-Firouzjah R, Farshidfar Z, Taleinezhad N, Ansari L, Razmkon A, Banaei A, Mehdizadeh A. Differentiating Between Low- and High-grade Glioma Tumors Measuring Apparent Diffusion Coefficient Values in Various Regions of the Brain. Oman Med J 2021; 36:e251. [PMID: 33936779 PMCID: PMC8077446 DOI: 10.5001/omj.2021.59] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 08/31/2020] [Indexed: 11/03/2022] Open
Abstract
Objectives Our study aimed to apply the apparent diffusion coefficient (ADC) values to quantify the differences between low- and high-grade glioma tumors. Methods We conducted a multicenter, retrospective study between September to December 2019. Magnetic resonance imaging (MRI) diffusion-weighted images (DWIs), and the pathologic findings of 56 patients with glioma tumors (low grade = 28 and high grade = 28) were assessed to measure the ADC values in the tumor center, tumor edema, boundary area between tumor with normal tissue, and inside the healthy hemisphere. These values were compared between the two groups, and cut-off values were calculated using the receiver operating characteristic curve. Results We saw significant differences between the mean ADC values measured in the tumor center and edema between high- and low-grade tumors (p< 0.005). The ADC values in the boundary area between tumors with normal tissue and inside healthy hemisphere did not significantly differ in the groups. The ADC values at tumor center and edema were higher than 1.12 × 10-3 mm2/s (sensitivity = 100% and specificity = 96.0%) and 1.15 × 10-3 mm2/s (sensitivity = 75.0% and specificity = 64.0%), respectively, could be classified as low-grade tumors. Conclusions The ADC values from the MRI DWIs in the tumor center and edema could be used as an appropriate method for investigating the differences between low- and high-grade glioma tumors. The ADC values in the boundary area and healthy tissues had no diagnostic values in grading the glioma tumors.
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Affiliation(s)
- Farideh Momeni
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.,Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Razzagh Abedi-Firouzjah
- Department of Medical Physics, Radiobiology and Radiation Protection, Babol University of Medical Sciences, Babol, Iran
| | - Zahra Farshidfar
- Radiology Technology Department, School of Paramedicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nastaran Taleinezhad
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Leila Ansari
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ali Razmkon
- Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amin Banaei
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.,Department of Radiology, Faculty of Paramedical Sciences, AJA University of Medical Sciences, Tehran, Iran
| | - Alireza Mehdizadeh
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.,Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
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Li Y, Kim M, Lawrence TS, Parmar H, Cao Y. Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma. ACTA ACUST UNITED AC 2021; 6:34-43. [PMID: 32280748 PMCID: PMC7138521 DOI: 10.18383/j.tom.2020.00018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Apparent diffusion coefficient has limits to differentiate solid tumor from normal tissue or edema in glioblastoma (GBM). This study investigated a microstructure model (MSM) in GBM using a clinically available diffusion imaging technique. The MSM was modified to integrate with bi-polar diffusion gradient waveforms, and applied to 30 patients with newly diagnosed GBM. Diffusion-weighted (DW) images acquired on a 3 T scanner with b-values from 0 to 2500 s/mm2 were fitted in volumes of interest (VOIs) of solid tumor to obtain the apparent restriction size of intracellular water (ARS), the fractional volume of intracellular water (Vin), and extracellular (Dex) water diffusivity. The parameters in solid tumor were compared with those of other tissue types by Students’ t test. For comparison, DW images were fitted by conventional mono-exponential and bi-exponential models. ARS, Dex, and Vin from the MSM in tumor VOIs were significantly greater than those in WM, GM, and edema (P values of .01–.001). ARS values in solid tumors (from 21.6 to 34.5 um) had absolutely no overlap with those in all other tissue types (from 0.9 to 3.5 um). Vin values showed a descending order from solid tumor (from 0.32 to 0.52) to WM, GM, and edema (from 0.05 to 0.25), consisting with the descending cellularity in these tissue types. The parameters from mono-exponential and bi-exponential models could not significantly differentiate solid tumor from all other tissue types, particularly from edema. Further development and histopathological validation of the MSM will warrant its role in clinical management of GBM.
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Affiliation(s)
- Yuan Li
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Michelle Kim
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Theodore S Lawrence
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Hemant Parmar
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Yue Cao
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
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Karaman MM, Zhang J, Xie KL, Zhu W, Zhou XJ. Quartile histogram assessment of glioma malignancy using high b-value diffusion MRI with a continuous-time random-walk model. NMR IN BIOMEDICINE 2021; 34:e4485. [PMID: 33543512 DOI: 10.1002/nbm.4485] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/15/2021] [Indexed: 06/12/2023]
Abstract
The purpose of this study is to investigate the feasibility of using a continuous-time random-walk (CTRW) diffusion model, together with a quartile histogram analysis, for assessing glioma malignancy by probing tissue heterogeneity as well as cellularity. In this prospective study, 91 patients (40 females, 51 males) with histopathologically proven gliomas underwent MRI at 3 T. The cohort included 42 grade II (GrII), 19 grade III (GrIII) and 29 grade IV (GrIV) gliomas. Echo-planar diffusion-weighted imaging was conducted using 17 b-values (0-4000 s/mm2 ). Three CTRW model parameters, including an anomalous diffusion coefficient Dm , and two parameters related to temporal and spatial diffusion heterogeneity α and β, respectively, were obtained. The mean parameter values within the tumor regions of interest (ROIs) were computed by utilizing the first quartile of the histograms as well as the full ROI for comparison. A Bonferroni-Holm-corrected Mann-Whitney U-test was used for the group comparisons. Individual and combinations of the CTRW parameters were evaluated for the characterization of gliomas with a receiver operating characteristic analysis. All first-quartile mean CTRW parameters yielded significant differences (p-values < 0.05) between pair-wise comparisons of GrII (Dm : 1.14 ± 0.37 μm2 /ms; α: 0.904 ± 0.03, β: 0.913 ± 0.06), GrIII (Dm : 0.88 ± 0.21 μm2 /ms; α: 0.888 ± 0.01, β: 0.857 ± 0.06) and GrIV gliomas (Dm : 0.73 ± 0.22 μm2 /ms; α: 0.878 ± 0.01; β: 0.791 ± 0.07). The highest sensitivity, specificity, accuracy and area-under-the-curve of using the combinations of the first-quartile parameters were 84.2%, 78.5%, 75.4% and 0.76 for GrII and GrIII classification; 86.2%, 89.4%, 75% and 0.76 for GrIII and GrIV classification; and 86.2%, 85.7%, 84.5% and 0.90 for GrII and GrIV classification, respectively. Quartile-based analysis produced higher accuracy and area-under-the-curve than the full ROI-based analysis in all classifications. The CTRW diffusion model, together with a quartile-based histogram analysis, offers a new way for probing tumor structural heterogeneity at a subvoxel level, and has potential for in vivo assessment of glioma malignancy to complement histopathology.
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Affiliation(s)
- M Muge Karaman
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Jiaxuan Zhang
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Karen L Xie
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA
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10
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Karaman MM, Tang L, Li Z, Sun Y, Li JZ, Zhou XJ. In vivo assessment of Lauren classification for gastric adenocarcinoma using diffusion MRI with a fractional order calculus model. Eur Radiol 2021; 31:5659-5668. [PMID: 33616764 DOI: 10.1007/s00330-021-07694-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 12/21/2020] [Accepted: 01/18/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate the performance of a fractional order calculus (FROC) diffusion model for imaging-based assessment of Lauren classification in gastric adenocarcinoma. METHODS In this study, 43 patients (15 females, 28 males) with gastric adenocarcinoma underwent MRI at 1.5 T. According to pathology-based Lauren classification, 10 patients had diffuse-type, 20 had intestinal-type, and 13 had mixed-type lesions. The diffuse and mixed types were combined as diffuse-and-mixed type to be differentiated from the intestinal type using diffusion MRI. Diffusion-weighted images were acquired by using eleven b-values (0-2000 s/mm2). Three FROC model parameters comprising diffusion coefficient D, intravoxel diffusion heterogeneity β, and a microstructural quantity μ, together with a conventional apparent diffusion coefficient (ADC), were estimated. The mean parameter values in the tumour were computed by using a percentile histogram analysis. Individual or linear combinations of the mean parameters in the tumour were used to differentiate the diffuse-and-mixed type from the intestinal type using descriptive statistics and receiver operating characteristic (ROC) analyses. RESULTS Significant differences were observed between diffuse-and-mixed-type and intestinal-type lesions in D (0.99 ± 0.20 μm2/ms vs. 1.11 ± 0.23 μm2/ms; p = 0.036), β (0.37 ± 0.08 vs. 0.43 ± 0.11; p = 0.043), μ (7.92 ± 2.79 μm vs. 9.87 ± 1.52 μm; p = 0.038), and ADC (0.81 ± 0.34 μm2/ms vs. 0.96 ± 0.19 μm2/ms; p = 0.033). Among the individual parameters, μ produced the largest area under the ROC curve (0.739). The combinations of (D, β, μ) and (β and μ) produced the best overall performance with a sensitivity of 0.739, specificity of 0.750, accuracy of 0.744, and area under the curve of 0.793 (95% confidence interval: 0.657-0.929). CONCLUSION Diffusion MRI with the FROC model holds promise for non-invasive assessment of Lauren classification for gastric adenocarcinoma. KEY POINTS • High b-value diffusion MRI with a FROC model that is sensitive to tissue microstructures can differentiate the diffuse-and-mixed type from intestinal type of gastric adenocarcinoma. • The combination of FROC parameters produced the best result for distinguishing the diffuse-and-mixed type from the intestinal type with an area under the receiver operating characteristic curve of 0.793. • The FROC model parameters, individually or conjointly, hold promise for repeated, non-invasive evaluations of gastric adenocarcinoma at various time points throughout disease progression or regression to complement conventional Lauren classification.
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Affiliation(s)
- M Muge Karaman
- Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Lei Tang
- Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ziyu Li
- Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing, China
| | - Yu Sun
- Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jia-Zheng Li
- Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA. .,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA. .,Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA. .,Center for Magnetic Resonance Research, University of Illinois at Chicago, 2242 West Harrison Street, Suite 103, M/C 831, Chicago, IL, 60612, USA.
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11
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Du L, Zhao Z, Xu B, Gao W, Liu X, Chen Y, Wang Y, Liu J, Liu B, Sun S, Ma G, Gao J. Anisotropy of Anomalous Diffusion Improves the Accuracy of Differentiating and Grading Alzheimer's Disease Using Novel Fractional Motion Model. Front Aging Neurosci 2020; 12:602510. [PMID: 33328977 PMCID: PMC7710869 DOI: 10.3389/fnagi.2020.602510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 10/19/2020] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose: Recent evidence shows that the fractional motion (FM) model may be a more appropriate model for describing the complex diffusion process of water in brain tissue and has shown to be beneficial in clinical applications of Alzheimer's disease (AD). However, the FM model averaged the anomalous diffusion parameter values, which omitted the impacts of anisotropy. This study aimed to investigate the potential feasibility of anisotropy of anomalous diffusion using the FM model for distinguishing and grading AD patients. Methods: Twenty-four patients with AD and 11 matched healthy controls were recruited, diffusion MRI was obtained from all participants and analyzed using the FM model. Generalized fractional anisotropy (gFA), an anisotropy metric, was introduced and the gFA values of FM-related parameters, Noah exponent (α) and the Hurst exponent (H), were calculated and compared between the healthy group and AD group and between the mild AD group and moderate AD group. The receiver-operating characteristic (ROC) analysis and the multivariate logistic regression analysis were used to assess the diagnostic performances of the anisotropy values and the directionally averaged values. Results: The gFA(α) and gFA(H) values of the moderate AD group were higher than those of the mild AD group in left hippocampus. The gFA(α) value of the moderate AD group was significantly higher than that of the healthy control group in both the left and right hippocampus. The gFA(ADC) values of the moderate AD group were significantly lower than those of the mild AD group and healthy control group in the right hippocampus. Compared with the gFA(α), gFA(H), α, and H, the ROC analysis showed larger areas under the curves for combination of α + gFA(α) and the combination of H + gFA(H) in differentiating the mild AD and moderate AD groups, and larger area under the curves for combination of α + gFA(α) in differentiating the healthy controls and AD groups. Conclusion: The anisotropy of anomalous diffusion could significantly differentiate and grade patients with AD, and the diagnostic performance was improved when the anisotropy metric was combined with commonly used directionally averaged values. The utility of anisotropic anomalous diffusion may provide novel insights to profoundly understand the neuropathology of AD.
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Affiliation(s)
- Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zifang Zhao
- Department of Anesthesiology, Peking University First Hospital, Peking University, Beijing, China
| | - Boyan Xu
- Beijing Intelligent Brain Cloud Inc., Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Liu
- Department of Ultrasound Diagnosis, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Shilong Sun
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiahong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
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12
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Efficiency of High and Standard b Value Diffusion-Weighted Magnetic Resonance Imaging in Grading of Gliomas. JOURNAL OF ONCOLOGY 2020; 2020:6942406. [PMID: 33005190 PMCID: PMC7509551 DOI: 10.1155/2020/6942406] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 09/02/2020] [Accepted: 09/07/2020] [Indexed: 02/06/2023]
Abstract
Background Glioma is the most common fatal malignant tumor of the CNS. Early detection of glioma grades based on diffusion-weighted imaging (DWI) properties is considered one of the most recent noninvasive promising tools in the assessment of glioma grade and could be helpful in monitoring patient prognosis and response to therapy. Aim This study aimed to investigate the accuracy of DWI at both standard and high b values (b = 1000 s/mm2 and b = 3000 s/mm2) to distinguish high-grade glioma (HGG) from low-grade glioma (LGG) in clinical practice based on histopathological results. Materials and Methods Twenty-three patients with glioma had DWI at l.5 T MR using two different b values (b = 1000 s/mm2 and b = 3000 s/mm2) at Al-Shifa Medical Complex after obtaining ethical and administrative approvals, and data were collected from March 2019 to March 2020. Minimum, maximum, and mean of apparent diffusion coefficient (ADC) values were measured through drawing region of interest (ROI) on a solid part at ADC maps. Data were analyzed by using the MedCalc analysis program, version 19.0.4, receiver operating characteristic (ROC) curve analysis was done, and optimal cutoff values for grading gliomas were determined. Sensitivity and specificity were also calculated. Results The obtained results showed the ADCmean, ADCratio, ADCmax, and ADCmin were performed to differentiate between LGG and HGG at both standard and high b values. Moreover, ADC values were inversely proportional to glioma grade, and these differences are more obvious at high b value. Minimum ADC values using standard b value were 1.13 ± 0.17 × 10−3 mm2/s, 0.89 ± 0.85 × 10−3 mm2/s, and 0.82 ± 0.17 × 10−3 mm2/s for grades II, III, and IV, respectively. Concerning high b value, ADCmin values were 0.76 ± 0.07 × 10−3 mm2/s, 0.61 ± 0.01 × 10−3 mm2/s, and 0.48 ± 0.07 × 10−3 mm2/s for grades II, III, and IV, respectively. ADC values were inversely correlated with results of glioma grades, and the correlation was stronger at ADC3000 (r = −0.722, P ≤ 0.001). The ADC3000 achieved the highest diagnostic accuracy with an area under the curve (AUC) of 0.618, 100% sensitivity, 85.7% specificity, and 85.7% accuracy for glioma grading at a cutoff point of ≤0.618 × 10−3 mm2/s. The high b value showed stronger agreement with histopathology compared with standard b value results (k = 0.89 and 0.79), respectively. Conclusion The ADC values decrease with an increase in tumor cellularity. Meanwhile, high b value provides better tissue contrast by reflecting more tissue diffusivity. Therefore, ADC-derived parameters at high b value are more useful in the grading of glioma than those obtained at standard b value. They might be a better surrogate imaging sequence in the preoperative evaluation of gliomas.
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13
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Liu B, Ma WL, Zhang GW, Sun Z, Wei MQ, Hou WH, Hou BX, Wei LC, Huan Y. Potentialities of multi-b-values diffusion-weighted imaging for predicting efficacy of concurrent chemoradiotherapy in cervical cancer patients. BMC Med Imaging 2020; 20:97. [PMID: 32799809 PMCID: PMC7429470 DOI: 10.1186/s12880-020-00496-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 08/06/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To testify whether multi-b-values diffusion-weighted imaging (DWI) can be used to ultra-early predict treatment response of concurrent chemoradiotherapy (CCRT) in cervical cancer patients and to assess the predictive ability of concerning parameters. METHODS Fifty-three patients with biopsy proved cervical cancer were retrospectively recruited in this study. All patients underwent pelvic multi-b-values DWI before and at the 3rd day during treatment. The apparent diffusion coefficient (ADC), true diffusion coefficient (Dslow), perfusion-related pseudo-diffusion coefficient (Dfast), perfusion fraction (f), distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index(α) were generated by mono-exponential, bi-exponential and stretched exponential models. Treatment response was assessed based on Response Evaluation Criteria in Solid Tumors (RECIST v1.1) at 1 month after the completion of whole CCRT. Parameters were compared using independent t test or Mann-Whitney U test as appropriate. Receiver operating characteristic (ROC) curves was used for statistical evaluations. RESULTS ADC-T0 (p = 0.02), Dslow-T0 (p < 0.01), DDC-T0 (p = 0.03), ADC-T1 (p < 0.01), Dslow-T1 (p < 0.01), ΔADC (p = 0.04) and Δα (p < 0.01) were significant lower in non-CR group patients. ROC analyses showed that ADC-T1 and Δα exhibited high prediction value, with area under the curves of 0.880 and 0.869, respectively. CONCLUSIONS Multi-b-values DWI can be used as a noninvasive technique to assess and predict treatment response in cervical cancer patients at the 3rd day of CCRT. ADC-T1 and Δα can be used to differentiate good responders from poor responders.
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Affiliation(s)
- Bing Liu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Wan-Ling Ma
- Department of radiology, Longgang District People's Hospital, Shenzhen, Guangdong, P. R. China, 518172
| | - Guang-Wen Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Zhen Sun
- Department of Orthopaedics, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Meng-Qi Wei
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Wei-Huan Hou
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Bing-Xin Hou
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Li-Chun Wei
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Yi Huan
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032.
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Xing X, Zhang J, Chen Y, Zhao Q, Lang N, Yuan H. Application of monoexponential, biexponential, and stretched-exponential models of diffusion-weighted magnetic resonance imaging in the differential diagnosis of metastases and myeloma in the spine-Univariate and multivariate analysis of related parameters. Br J Radiol 2020; 93:20190891. [PMID: 32462885 DOI: 10.1259/bjr.20190891] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To explore the value of related parameters in monoexponential, biexponential, and stretched-exponential models of diffusion-weighted imaging (DWI) in differentiating metastases and myeloma in the spine. METHODS 53 metastases and 16 myeloma patients underwent MRI with 10 b-values (0-1500 s/mm2). Parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), the distribution diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α) from DWI were calculated. The independent sample t test and the Mann-Whiney U test were used to compare the statistical difference of the parameter values between the two. Receiver operating characteristics (ROC) curve analysis was used to identify the diagnostic efficacy. Then substituted each parameter into the decision tree model and logistic regression model, identified meaningful parameters, and evaluated their joint diagnostic performance. RESULTS The ADC, D, and α values of metastases were higher than those of myeloma, whereas the D* value was lower than that of myeloma, and the difference was significant (p < 0.05); the area under the ROC curve for the above parameters was 0.661, 0.710, 0.781, and 0.743, respectively. There was no significant difference in the f and DDC values (p > 0.05). D and α were found to conform to the decision tree model, and the accuracy of model diagnosis was 84.1%. ADC and α were found to conform to the logistic regression model, and the accuracy was 87.0%. CONCLUSION The 3 models of DWI have certain values indifferentiating metastases and myeloma in spine, and the diagnostic performance of ADC, D, α and D*was better. Combining ADC with α may markedly aid in the differential diagnosis of the two. ADVANCES IN KNOWLEDGE Monoexponential, biexponential, and stretched-exponential models can offer additional information in the differential diagnosis of metastases and myeloma in the spine. Decision tree model and logistic regression model are effective methods to help further distinguish the two.
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Affiliation(s)
- Xiaoying Xing
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| | - Jiahui Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| | - Qiang Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
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Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging: a comparative study of mono-, bi-, and stretched-exponential diffusion models. Neuroradiology 2020; 62:815-823. [PMID: 32424712 PMCID: PMC7311374 DOI: 10.1007/s00234-020-02456-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 05/05/2020] [Indexed: 12/13/2022]
Abstract
Purpose Diffusion-weighted imaging (DWI) plays an important role in the preoperative assessment of gliomas; however, the diagnostic performance of histogram-derived parameters from mono-, bi-, and stretched-exponential DWI models in the grading of gliomas has not been fully investigated. Therefore, we compared these models’ ability to differentiate between high-grade and low-grade gliomas. Methods This retrospective study included 22 patients with diffuse gliomas (age, 23–74 years; 12 males; 11 high-grade and 11 low-grade gliomas) who underwent preoperative 3 T-magnetic resonance imaging from October 2014 to August 2019. The apparent diffusion coefficient was calculated from the mono-exponential model. Using 13 b-values, the true-diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction were obtained from the bi-exponential model, and the distributed-diffusion coefficient and heterogeneity index were obtained from the stretched-exponential model. Region-of-interests were drawn on each imaging parameter map for subsequent histogram analyses. Results The skewness of the apparent diffusion, true-diffusion, and distributed-diffusion coefficients was significantly higher in high-grade than in low-grade gliomas (0.67 ± 0.67 vs. − 0.18 ± 0.63, 0.68 ± 0.74 vs. − 0.08 ± 0.66, 0.63 ± 0.72 vs. − 0.15 ± 0.73; P = 0.0066, 0.0192, and 0.0128, respectively). The 10th percentile of the heterogeneity index was significantly lower (0.77 ± 0.08 vs. 0.88 ± 0.04; P = 0.0004), and the 90th percentile of the perfusion fraction was significantly higher (12.64 ± 3.44 vs. 7.14 ± 1.70%: P < 0.0001), in high-grade than in low-grade gliomas. The combination of the 10th percentile of the true-diffusion coefficient and 90th percentile of the perfusion fraction showed the best area under the receiver operating characteristic curve (0.96). Conclusion The bi-exponential model exhibited the best diagnostic performance for differentiating high-grade from low-grade gliomas. Electronic supplementary material The online version of this article (10.1007/s00234-020-02456-2) contains supplementary material, which is available to authorized users.
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Guo R, Yang SH, Lu F, Han ZH, Yan X, Fu CX, Zhao ML, Lin J. Evaluation of intratumoral heterogeneity by using diffusion kurtosis imaging and stretched exponential diffusion-weighted imaging in an orthotopic hepatocellular carcinoma xenograft model. Quant Imaging Med Surg 2019; 9:1566-1578. [PMID: 31667142 DOI: 10.21037/qims.2019.08.18] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background To investigate the value of diffusion kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) with a stretched exponential model (SEM) in the evaluation of tumor heterogeneity in an orthotopic hepatocellular carcinoma (HCC) xenograft model. Methods Thirty orthotopic HCC xenograft nude mice models were established and randomly divided into two groups, the sorafenib induction group (n=15) and control group (n=15). Every mouse in each group underwent MRI with DKI and SEM on a 1.5T MR scanner at 7, 14, and 21 days after sorafenib intervention. DKI and SEM parameters including mean kurtosis (MK), mean diffusivity (MD), α, and distributed diffusion coefficient (DDC) were measured, calculated, and compared between the two groups and among different time points. Sequential correlations between histopathological results including necrotic fraction (NF), micro-vessel density (MVD), Ki-67 index, standard deviation (SD), and kurtosis from hematoxylin-eosin staining, and DKI and SEM parameters were analyzed. Results MK, MD, and DDC of HCC in the sorafenib induction group were significantly higher than those in the control group at each time point (P<0.05), while α was significantly lower (P<0.05). Significantly positive correlations were found between MK and NF (r=0.693, P=0.010), SD (r =0.785, P=0.003), kurtosis (r=0.779, P=0.003), between MD and NF (r=0.794, P=0.003), SD (r=0.629, P=0.020), kurtosis (r=0.645, P=0.018), and between DDC and NF (r=0.800, P=0.003), SD (r=0.636, P=0.020), kurtosis (r=0.664, P=0.016), and significantly negative correlations were observed between α and NF (r=-0.704, P=0.009), SD (r=-0.754, P=0.003), and kurtosis (r=-0.792, P=0.003) in the sorafenib induction group. Conclusions DKI and SEM parameters may be potentially useful for evaluating intratumoral heterogeneity in HCC.
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Affiliation(s)
- Ran Guo
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Shuo-Hui Yang
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China
| | - Fang Lu
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China
| | - Zhi-Hong Han
- Department of Pathology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, Shanghai 201318, China
| | - Cai-Xia Fu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen 518057, China
| | - Meng-Long Zhao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Shanghai Institute of Medical Imaging, Shanghai 200032, China
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Jin YN, Zhang Y, Cheng JL, Zheng DD, Hu Y. Monoexponential, Biexponential, and stretched-exponential models using diffusion-weighted imaging: A quantitative differentiation of breast lesions at 3.0T. J Magn Reson Imaging 2019; 50:1461-1467. [PMID: 30919518 DOI: 10.1002/jmri.26729] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Revised: 03/11/2019] [Accepted: 03/12/2019] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Diffusion-weighted imaging (DWI) plays an important role in the differentiation of malignant and benign breast lesions. PURPOSE To investigate the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched-exponential DWI models in the differential diagnosis of breast lesions. STUDY TYPE Prospective. POPULATION Sixty-one patients (age range: 25-68 years old; mean age: 46 years old) with 31 malignant lesions, 42 benign lesions, and 28 normal breast tissues diagnosed initially by clinical palpation, ultrasonography, or conventional mammography were enrolled in the study from January to September 2016. FIELD STRENGTH 3.0T MR scanner, T1 WI, T2 WI, DWI (conventional and multi-b values), dynamic contrast-enhanced. ASSESSMENT The apparent diffusion coefficient (ADC) was calculated by monoexponential analysis. The diffusion coefficient (ADCslow ), pseudodiffusion coefficient (ADCfast ), and perfusion fraction (f) were calculated using the biexponential model. The distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) were obtained using a stretched-exponential model. All parameters were compared for malignant tumors, benign tumors, and normal breast tissues. A receiver operating characteristic curve was used to compare the ability of these parameters, in order to differentiate benign and malignant breast lesions. STATISTICAL TESTS All statistical analyses were performed using statistical software (SPSS). RESULTS ADC, ADCslow , f, DDC, and α values were significantly lower in malignant tumors when compared with normal breast tissues and benign tumors (P < 0.05). However, ADC and f had higher area under the receiver operating characteristic curve (AUC) values (0.889 and 0.919, respectively). DATA CONCLUSION The parameters derived from the biexponential and stretched-exponential DWI could provide additional information for differentiating between benign and malignant breast tumors when compared with conventional diffusion parameters. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;50:1461-1467.
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Affiliation(s)
- Ya-Nan Jin
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Zhang
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing-Liang Cheng
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | | | - Ying Hu
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Tang L, Zhou XJ. Diffusion MRI of cancer: From low to high b-values. J Magn Reson Imaging 2018; 49:23-40. [PMID: 30311988 DOI: 10.1002/jmri.26293] [Citation(s) in RCA: 133] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 07/20/2018] [Accepted: 07/23/2018] [Indexed: 12/14/2022] Open
Abstract
Following its success in early detection of cerebral ischemia, diffusion-weighted imaging (DWI) has been increasingly used in cancer diagnosis and treatment evaluation. These applications are propelled by the rapid development of novel diffusion models to extract biologically valuable information from diffusion-weighted MR signals, and significant advances in MR hardware that has enabled image acquisition with high b-values. This article reviews recent technical developments and clinical applications in cancer imaging using DWI, with a special emphasis on high b-value diffusion models. The article is organized in four sections. First, we provide an overview of diffusion models that are relevant to cancer imaging. The model parameters are discussed in relation to three tissue properties-cellularity, vascularity, and microstructures. An emphasis is placed on characterization of microstructural heterogeneity, given its novelty and close relevance to cancer. Second, we illustrate diffusion MR clinical applications in each of the following three categories: 1) cancer detection and diagnosis; 2) cancer grading, staging, and classification; and 3) cancer treatment response prediction and evaluation. Third, we discuss several practical issues, including selection of image acquisition parameters, reproducibility and reliability, motion management, image distortion, etc., that are commonly encountered when applying DWI to cancer in clinical settings. Lastly, we highlight a few ongoing challenges and provide some possible future directions, particularly in the area of establishing standards via well-organized multicenter clinical trials to accelerate clinical translation of advanced DWI techniques to improving cancer care on a large scale. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:23-40.
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Affiliation(s)
- Lei Tang
- Department of Radiology, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research, Beijing, China
| | - Xiaohong Joe Zhou
- Center for MR Research and Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
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19
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Zhang J, Weaver TE, Zhong Z, Nisi RA, Martin KR, Steffen AD, Karaman MM, Zhou XJ. White matter structural differences in OSA patients experiencing residual daytime sleepiness with high CPAP use: a non-Gaussian diffusion MRI study. Sleep Med 2018; 53:51-59. [PMID: 30445240 DOI: 10.1016/j.sleep.2018.09.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 08/19/2018] [Accepted: 09/20/2018] [Indexed: 01/08/2023]
Abstract
OBJECTIVES To investigate factors associated with residual sleepiness in patients who were highly adherent to continuous positive airway pressure (CPAP). Nocturnal inactivity, comorbidities, concomitant medications, and, in particular, white matter (WM) differences using diffusion magnetic resonance imaging (MRI) were explored using a continuous-time random-walk (CTRW) model. METHODS Twenty-seven male patients (30-55 years of age) with obstructive sleep apnea (OSA) received CPAP as the only treatment (CPAP ≥ 6 h/night) for at least 30 days. Based on the Psychomotor Vigilance Task (PVT) results, participants were divided into a non-sleepy group (lapses ≤ 5; n = 18) and a sleepy group (lapses > 5; n = 9). Mean nocturnal inactivity (sleep proxy) was measured using actigraphy for one week. Diffusion-weighted imaging (DWI) with high b-values, as well as diffusion tensor imaging (DTI), was performed on a 3 T MRI scanner. The DWI dataset was analyzed using the CTRW model that yielded three parameters: temporal diffusion heterogeneity α, spatial diffusion heterogeneity β, and an anomalous diffusion coefficient Dm. The differences in α, β, and Dm between the two groups were investigated by a whole-brain analysis using tract-based spatial statistics (TBSS), followed by a regional analysis on individual fiber tracts using a standard parcellation template. Results from the CTRW model were compared with those obtained from DTI. The three CTRW parameters were also correlated with the clinical assessment scores, Epworth Sleepiness Scale (ESS), PVT lapses, and PVT mean reaction time (MRT) in specific fiber tracts. RESULTS There were no differences between groups in mean sleep duration, comorbidities, and the number or type of medications, including alerting and sedating medications. In the whole-brain DWI analysis, the sleepy group showed higher α (17.27% of the WM voxels) and Dm (17.14%) when compared to the non-sleepy group (P < 0.05), whereas no significant difference in β was observed. In the regional fiber analysis, the sleepy and non-sleepy groups showed significant differences in α, β, or their combinations in a total of 12 fiber tracts; whereas similar differences were not observed in DTI parameters, when age was used as a covariate. Additionally, moderate to strong correlations between the CTRW parameters (α, β, or Dm) and the sleepiness assessment scores (ESS, PVT lapses, or PVT MRT) were observed in specific fiber tracts (|R| = 0.448-0.654, P = 0.0003-0.019). CONCLUSIONS The observed differences in the CTRW parameters between the two groups indicate that WM alterations can be a possible mechanism to explain reversible versus residual sleepiness observed in OSA patients with identical high level of CPAP use. The moderate to strong correlations between the CTRW parameters and the clinical scores suggest the possibility of developing objective and quantitative imaging markers to complement clinical assessment of OSA patients.
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Affiliation(s)
- Jiaxuan Zhang
- Center for MR Research, University of Illinois, Chicago, IL, USA; Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Terri E Weaver
- Department of Biobehavioral Health Science, University of Illinois, Chicago, IL, USA; Center for Sleep and Health, College of Nursing, University of Illinois, Chicago, IL, USA
| | - Zheng Zhong
- Center for MR Research, University of Illinois, Chicago, IL, USA; Department of Bioengineering, College of Medicine, University of Illinois, Chicago, IL, USA
| | - Robyn A Nisi
- Department of Biobehavioral Health Science, University of Illinois, Chicago, IL, USA
| | - Kelly R Martin
- Department of Biobehavioral Health Science, University of Illinois, Chicago, IL, USA
| | - Alana D Steffen
- Department of Health Systems Science, University of Illinois, Chicago, IL, USA
| | - M Muge Karaman
- Center for MR Research, University of Illinois, Chicago, IL, USA; Department of Bioengineering, College of Medicine, University of Illinois, Chicago, IL, USA
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois, Chicago, IL, USA; Department of Radiology, College of Medicine, University of Illinois, Chicago, IL, USA; Department of Bioengineering, College of Medicine, University of Illinois, Chicago, IL, USA; Department of Neurosurgery, College of Medicine, University of Illinois, Chicago, IL, USA.
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20
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Chakhoyan A, Woodworth DC, Harris RJ, Lai A, Nghiemphu PL, Liau LM, Pope WB, Cloughesy TF, Ellingson BM. Mono-exponential, diffusion kurtosis and stretched exponential diffusion MR imaging response to chemoradiation in newly diagnosed glioblastoma. J Neurooncol 2018; 139:651-659. [PMID: 29855771 PMCID: PMC6126989 DOI: 10.1007/s11060-018-2910-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 05/22/2018] [Indexed: 01/18/2023]
Abstract
PURPOSE To quantify changes and prognostic value of diffusion MRI measurements obtained using mono-exponential, diffusion kurtosis imaging (DKI) and stretched exponential (SE) models prior and after chemoradiation in newly diagnosed glioblastoma (GBM). METHODS Diffusion-weighted images (DWIs) were acquired in twenty-three patients following surgery, prior chemoradiation and within 7 days following completion of treatment, using b-values ranging from 0 to 5000s/mm2. Mono-exponential diffusion (apparent diffusion coefficient: ADC), isotropic (non-directional) DKI model with apparent diffusivity (Dapp) and kurtosis (Kapp) estimates as well as SE model with distributed-diffusion coefficient (DDC) and mean intra-voxel heterogeneity (α) were computed for all patients prior and after chemoradiation. Median values were calculated for normal appearing white matter (NAWM) and contrast-enhancing tumor (CET). The magnitudes of diffusion change prior and after chemoradiation were used to predict overall survival (OS). RESULTS Diffusivity in NAWM was consistent for all diffusion measures during chemoradiation, while diffusivity measurements (ADC, Dapp and DDC) within CET changed significantly. A strong positive correlation existed between ADC, Dapp, and DDC measurements prior to chemoradiation; however, this association was weak following chemoradiation, suggesting a more complex microstructural environment after cytotoxic therapy. When combined with baseline tumor volume and MGMT status, age and ADC changes added significant prognostic values, whereas more complex diffusion models did not show significant value in predicting OS. CONCLUSIONS Despite increased tissue complexity following chemoradiation, advanced diffusion models have longer acquisition times, provide largely comparable measures of diffusivity, and do not appear to provide additional prognostic value compared to mono-exponential ADC maps.
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Affiliation(s)
- Ararat Chakhoyan
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Davis C Woodworth
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biomedical Physics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Robert J Harris
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Brain Tumor Imaging Laboratory (BTIL), Biomedical Physics, Psychiatry, and Bioengineering, Departments of Radiological Sciences and Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
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21
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Chen X, Jiang J, Shen N, Zhao L, Zhang J, Qin Y, Zhang S, Li L, Zhu W. Stretched-exponential model diffusion-weighted imaging as a potential imaging marker in preoperative grading and assessment of proliferative activity of gliomas. Am J Transl Res 2018; 10:2659-2668. [PMID: 30210702 PMCID: PMC6129521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 07/08/2018] [Indexed: 06/08/2023]
Abstract
PURPOSE To assess the feasibility of using diffusion-weighted imaging (DWI) with the stretched-exponential model (SEM) for glioma grading and determining the correlations among parameters and proliferating cell nuclear antigen and Ki-67 expression. MATERIALS AND METHODS Mono-exponential model-DWI (MEM-DWI) and SEM-DWI were performed in 104 patients with pathologically proven gliomas. The patients were divided into the training set (n = 72) and test set (n = 32). Apparent diffusion coefficient (ADC), solid tumor distributed diffusion coefficient (DDC), and whole tumor α values were measured. These parameters were applied as cut-off values to determine the predictive accuracy. Proliferating cell nuclear antigen and Ki-67 expression correlated with all parameters. RESULTS Significant differences between low-grade gliomas (LGG) and high-grade gliomas (HGG) were observed for all parameters (P < 0.05), and significant differences in the ability of DDC to distinguish between any two glioma grades (P < 0.05) were also evident. DDC showed the highest sensitivity and specificity for glioma grading and was negatively correlated with Ki-67 and proliferating cell nuclear antigen expression. DDC also showed greater predictive accuracy than ADC and α. CONCLUSION SEM-DWI offers a better approach for glioma grading than MEM-DWI, and DDC may be a better imaging biomarker for grading and evaluating the proliferative activity of brain gliomas.
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Affiliation(s)
- Xiaowei Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Jingjing Jiang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Lingyun Zhao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Jiaxuan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Li Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
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22
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Hu YC, Yan LF, Sun Q, Liu ZC, Wang SM, Han Y, Tian Q, Sun YZ, Zheng DD, Wang W, Cui GB. Comparison between ultra-high and conventional mono b-value DWI for preoperative glioma grading. Oncotarget 2018; 8:37884-37895. [PMID: 28039453 PMCID: PMC5514959 DOI: 10.18632/oncotarget.14180] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 11/22/2016] [Indexed: 12/12/2022] Open
Abstract
To compare the efficacy of ultra-high and conventional mono-b-value DWI for glioma grading, in 109 pathologically confirmed glioma patients, ultra-high apparent diffusion coefficient (ADCuh)was calculated using a tri-exponential mode, distributed diffusion coefficients (DDCs) and α values were calculated using a stretched-exponential model, and conventional ADC values were calculated using a mono-exponential model. The efficacy and reliability of parameters for grading gliomas were investigated using receiver operating characteristic (ROC) curve and intra-class correlation (ICC) analyses, respectively. The ADCuh values differed (P < 0.001) between low-grade gliomas (LGGs; 0.436 ×10−3 mm2/sec) and high-grade gliomas (HGGs; 0.285 × 10−3 mm2/sec). DDC, a and various conventional ADC values were smaller in HGGs (all P ≤ 0.001, vs. LGGs). The ADCuh parameter achieved the highest diagnostic efficacy with an area under curve (AUC) of 0.993, 92.9% sensitivity and 98.8% specificity for glioma grading at a cutoff value of 0.362×10−3 mm2/sec. ADCuh measurement appears to be an easy-to-perform technique with good reproducibility (ICC = 0.9391, P < 0.001). The ADCuh value based in a tri-exponential model exhibited greater efficacy and reliability than other DWI parameters, making it a promising technique for glioma grading.
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Affiliation(s)
- Yu-Chuan Hu
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Lin-Feng Yan
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Qian Sun
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhi-Cheng Liu
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Shu-Mei Wang
- Department of Pathology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Yu Han
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Qiang Tian
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Ying-Zhi Sun
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Dan-Dan Zheng
- MR Research China, GE Healthcare China, Beijing, China
| | - Wen Wang
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Guang-Bin Cui
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
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Seo N, Chung YE, Park YN, Kim E, Hwang J, Kim MJ. Liver fibrosis: stretched exponential model outperforms mono-exponential and bi-exponential models of diffusion-weighted MRI. Eur Radiol 2018; 28:2812-2822. [PMID: 29404771 DOI: 10.1007/s00330-017-5292-z] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 12/14/2017] [Accepted: 12/27/2017] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To compare the ability of diffusion-weighted imaging (DWI) parameters acquired from three different models for the diagnosis of hepatic fibrosis (HF). METHODS Ninety-five patients underwent DWI using nine b values at 3 T magnetic resonance. The hepatic apparent diffusion coefficient (ADC) from a mono-exponential model, the true diffusion coefficient (D t ), pseudo-diffusion coefficient (D p ) and perfusion fraction (f) from a biexponential model, and the distributed diffusion coefficient (DDC) and intravoxel heterogeneity index (α) from a stretched exponential model were compared with the pathological HF stage. For the stretched exponential model, parameters were also obtained using a dataset of six b values (DDC#, α#). The diagnostic performances of the parameters for HF staging were evaluated with Obuchowski measures and receiver operating characteristics (ROC) analysis. The measurement variability of DWI parameters was evaluated using the coefficient of variation (CoV). RESULTS Diagnostic accuracy for HF staging was highest for DDC# (Obuchowski measures, 0.770 ± 0.03), and it was significantly higher than that of ADC (0.597 ± 0.05, p < 0.001), D t (0.575 ± 0.05, p < 0.001) and f (0.669 ± 0.04, p = 0.035). The parameters from stretched exponential DWI and D p showed higher areas under the ROC curve (AUCs) for determining significant fibrosis (≥F2) and cirrhosis (F = 4) than other parameters. However, D p showed significantly higher measurement variability (CoV, 74.6%) than DDC# (16.1%, p < 0.001) and α# (15.1%, p < 0.001). CONCLUSIONS Stretched exponential DWI is a promising method for HF staging with good diagnostic performance and fewer b-value acquisitions, allowing shorter acquisition time. KEY POINTS • Stretched exponential DWI provides a precise and accurate model for HF staging. • Stretched exponential DWI parameters are more reliable than D p from bi-exponential DWI model • Acquisition of six b values is sufficient to obtain accurate DDC and α.
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Affiliation(s)
- Nieun Seo
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Yong Eun Chung
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
- BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Yung Nyun Park
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Eunju Kim
- Philips Healthcare Korea, Sowoel-ro 272, Seoul, 04342, Korea
| | - Jinwoo Hwang
- Philips Healthcare Korea, Sowoel-ro 272, Seoul, 04342, Korea
| | - Myeong-Jin Kim
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
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Mahajan A, Deshpande SS, Thakur MH. Diffusion magnetic resonance imaging: A molecular imaging tool caught between hope, hype and the real world of “personalized oncology”. World J Radiol 2017; 9:253-268. [PMID: 28717412 PMCID: PMC5491653 DOI: 10.4329/wjr.v9.i6.253] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 03/08/2017] [Accepted: 04/19/2017] [Indexed: 02/06/2023] Open
Abstract
“Personalized oncology” is a multi-disciplinary science, which requires inputs from various streams for optimal patient management. Humongous progress in the treatment modalities available and the increasing need to provide functional information in addition to the morphological data; has led to leaping progress in the field of imaging. Magnetic resonance imaging has undergone tremendous progress with various newer MR techniques providing vital functional information and is becoming the cornerstone of “radiomics/radiogenomics”. Diffusion-weighted imaging is one such technique which capitalizes on the tendency of water protons to diffuse randomly in a given system. This technique has revolutionized oncological imaging, by giving vital qualitative and quantitative information regarding tumor biology which helps in detection, characterization and post treatment surveillance of the lesions and challenging the notion that “one size fits all”. It has been applied at various sites with different clinical experience. We hereby present a brief review of this novel functional imaging tool, with its application in “personalized oncology”.
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Tang L, Sui Y, Zhong Z, Damen FC, Li J, Shen L, Sun Y, Zhou XJ. Non-Gaussian diffusion imaging with a fractional order calculus model to predict response of gastrointestinal stromal tumor to second-line sunitinib therapy. Magn Reson Med 2017. [PMID: 28643387 DOI: 10.1002/mrm.26798] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE To demonstrate the clinical value of a non-Gaussian diffusion model using fractional order calculus (FROC) for early prediction of the response of gastrointestinal stromal tumor to second-line sunitinib targeted therapy. METHODS Fifteen patients underwent sunitinib treatment after imatinib resistance. Diffusion-weighted imaging with multiple b-values was performed before treatment (baseline) and 2 weeks (for early prediction of response) after initiating sunitinib treatment. Conventional MRI images at 12 weeks were used to determine the good and poor responders according to the modified Choi criteria for MRI. Diffusion coefficient D, fractional order parameter β (which correlates to intravoxel tissue heterogeneity), and a microstructural quantity µ were calculated using the FROC model. The FROC parameters and the longest diameter of the lesion, as well as their changes after 2 weeks of treatment, were compared between the good and poor responders. Additionally, the pretreatment FROC parameters were individually combined with the change in D (ΔD) using a logistic regression model to evaluate response to sunitinib treatment with a receiver operating characteristic analysis. RESULTS Forty-two good-responding and 32 poor-responding lesions were identified. Significant differences were detected in pretreatment β (0.67 versus 0.74, P = 0.011) and ΔD (45.7% versus 12.4%, P = 0.001) between the two groups. The receiver operating characteristic analysis showed that ΔD had a significantly higher predictive power than the tumor size change (area under the curve: 0.725 versus 0.580; 0.95 confidence interval). When ΔD was combined with pretreatment β, the area under the curve improved to 0.843 with a predictive accuracy of 75.7% (56 of 74). CONCLUSIONS The non-Gaussian FROC diffusion model showed clinical value in early prediction of gastrointestinal stromal tumor response to second-line sunitinib targeted therapy. The pretreatment FROC parameter β can increase the predictive accuracy when combined with the change in diffusion coefficient during treatment. Magn Reson Med 79:1399-1406, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Lei Tang
- Department of Radiology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research, Beijing, China.,Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Yi Sui
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Zheng Zhong
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Frederick C Damen
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Jian Li
- Department of Gastroenterology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research, Beijing, China
| | - Lin Shen
- Department of Gastroenterology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research, Beijing, China
| | - Yingshi Sun
- Department of Radiology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research, Beijing, China
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA
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Karaman MM, Wang H, Sui Y, Engelhard HH, Li Y, Zhou XJ. A fractional motion diffusion model for grading pediatric brain tumors. NEUROIMAGE-CLINICAL 2016; 12:707-714. [PMID: 27761401 PMCID: PMC5065039 DOI: 10.1016/j.nicl.2016.10.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 09/30/2016] [Accepted: 10/01/2016] [Indexed: 12/23/2022]
Abstract
Objectives To demonstrate the feasibility of a novel fractional motion (FM) diffusion model for distinguishing low- versus high-grade pediatric brain tumors; and to investigate its possible advantage over apparent diffusion coefficient (ADC) and/or a previously reported continuous-time random-walk (CTRW) diffusion model. Materials and methods With approval from the institutional review board and written informed consents from the legal guardians of all participating patients, this study involved 70 children with histopathologically-proven brain tumors (30 low-grade and 40 high-grade). Multi-b-value diffusion images were acquired and analyzed using the FM, CTRW, and mono-exponential diffusion models. The FM parameters, Dfm, φ, ψ (non-Gaussian diffusion statistical measures), and the CTRW parameters, Dm, α, β (non-Gaussian temporal and spatial diffusion heterogeneity measures) were compared between the low- and high-grade tumor groups by using a Mann-Whitney-Wilcoxon U test. The performance of the FM model for differentiating between low- and high-grade tumors was evaluated and compared with that of the CTRW and the mono-exponential models using a receiver operating characteristic (ROC) analysis. Results The FM parameters were significantly lower (p < 0.0001) in the high-grade (Dfm: 0.81 ± 0.26, φ: 1.40 ± 0.10, ψ: 0.42 ± 0.11) than in the low-grade (Dfm: 1.52 ± 0.52, φ: 1.64 ± 0.13, ψ: 0.67 ± 0.13) tumor groups. The ROC analysis showed that the FM parameters offered better specificity (88% versus 73%), sensitivity (90% versus 82%), accuracy (88% versus 78%), and area under the curve (AUC, 93% versus 80%) in discriminating tumor malignancy compared to the conventional ADC. The performance of the FM model was similar to that of the CTRW model. Conclusions Similar to the CTRW model, the FM model can improve differentiation between low- and high-grade pediatric brain tumors over ADC. The fractional motion (FM) diffusion model was applied to pediatric brain tumors. The FM model parameters can be sensitive to tissue microstructures. The FM model outperforms the mono-exponential diffusion model. The FM model performs similarly to the continuous-time random-walk (CTRW) model. Our results challenge those from recent biophysics studies in cell cultures.
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Affiliation(s)
- M. Muge Karaman
- Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yi Sui
- Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | | | - Yuhua Li
- Xinhua Hospital, Shanghai, China
- Correspondence to: Yuhua. Li, Department of Radiology, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, 1665 Kong Jiang Road, 200092 Shanghai, China.Department of RadiologyXinhua HospitalShanghai Jiaotong University School of Medicine1665 Kong Jiang RoadShanghai200092China
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
- Correspondence to: Xiaohong Joe Zhou, Center for Magnetic Resonance Research and Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois at Chicago, 2242 West Harrison Street, Suite 103, M/C 831, Chicago, IL 60612, USA.Center for Magnetic Resonance Research and Departments of Radiology, Neurosurgery, and BioengineeringUniversity of Illinois at Chicago2242 West Harrison StreetSuite 103M/C 831ChicagoIL60612USA
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Karaman MM, Sui Y, Wang H, Magin RL, Li Y, Zhou XJ. Differentiating low- and high-grade pediatric brain tumors using a continuous-time random-walk diffusion model at high b-values. Magn Reson Med 2016. [PMID: 26519663 DOI: 10.1002/mrm26012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2023]
Abstract
PURPOSE To demonstrate that a continuous-time random-walk (CTRW) diffusion model can improve diagnostic accuracy of differentiating low- and high-grade pediatric brain tumors. METHODS Fifty-four children with histopathologically confirmed brain tumors underwent diffusion MRI scans at 3Twith 12 b-values (0-4000 s/mm(2) ). The diffusion imageswere fit to a simplified CTRW model to extract anomalous diffusion coefficient, Dm , and temporal and spatial heterogeneity parameters, α and β, respectively. Using histopathology results as reference, a k-means clustering algorithm and a receiver operating characteristic (ROC) analysis were employed to determine the sensitivity, specificity, and diagnostic accuracy of the CTRW parameters in differentiating tumor grades. RESULTS Significant differences between the low- and high-grade tumors were observed in the CTRW parameters (p-values<0.001). The k-means analysis showed that the combination of three CTRW parameters produced higher diagnostic accuracy (85% vs. 75%) and specificity (83% vs. 54%) than the apparent diffusion coefficient (ADC) from a mono-exponential model. The ROC analysis revealed that any combination of the CTRW parameters gave a larger area under the curve (0.90-0.96) than using ADC (0.80). CONCLUSION With its sensitivity to intravoxel heterogeneity, the simplified CTRW model is useful for non-invasive grading of pediatric brain tumors, particularly when surgical biopsy is not feasible. Magn Reson Med 76:1149-1157, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- M Muge Karaman
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Yi Sui
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - He Wang
- Philips Research China, Shanghai, China
| | - Richard L Magin
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Yuhua Li
- Xinhua Hospital, Shanghai, China
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA.
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA.
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA.
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA.
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Sui Y, Xiong Y, Jiang J, Karaman MM, Xie KL, Zhu W, Zhou XJ. Differentiation of Low- and High-Grade Gliomas Using High b-Value Diffusion Imaging with a Non-Gaussian Diffusion Model. AJNR Am J Neuroradiol 2016; 37:1643-9. [PMID: 27256851 DOI: 10.3174/ajnr.a4836] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 02/22/2016] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Imaging-based tumor grading is highly desirable but faces challenges in sensitivity, specificity, and diagnostic accuracy. A recently proposed diffusion imaging method by using a fractional order calculus model offers a set of new parameters to probe not only the diffusion process itself but also intravoxel tissue structures, providing new opportunities for noninvasive tumor grading. This study aimed to demonstrate the feasibility of using the fractional order calculus model to differentiate low- from high-grade gliomas in adult patients and illustrate its improved performance over a conventional diffusion imaging method using ADC (or D). MATERIALS AND METHODS Fifty-four adult patients (18-70 years of age) with histology-proved gliomas were enrolled and divided into low-grade (n = 24) and high-grade (n = 30) groups. Multi-b-value diffusion MR imaging was performed with 17 b-values (0-4000 s/mm(2)) and was analyzed by using a fractional order calculus model. Mean values and SDs of 3 fractional order calculus parameters (D, β, and μ) were calculated from the normal contralateral thalamus (as a control) and the tumors, respectively. On the basis of these values, the low- and high-grade glioma groups were compared by using a Mann-Whitney U test. Receiver operating characteristic analysis was performed to assess the performance of individual parameters and the combination of multiple parameters for low- versus high-grade differentiation. RESULTS Each of the 3 fractional order calculus parameters exhibited a statistically higher value (P ≤ .011) in the low-grade than in the high-grade gliomas, whereas there was no difference in the normal contralateral thalamus (P ≥ .706). The receiver operating characteristic analysis showed that β (area under the curve = 0.853) produced a higher area under the curve than D (0.781) or μ (0.703) and offered a sensitivity of 87.5%, specificity of 76.7%, and diagnostic accuracy of 82.1%. CONCLUSIONS The study demonstrated the feasibility of using a non-Gaussian fractional order calculus diffusion model to differentiate low- and high-grade gliomas. While all 3 fractional order calculus parameters showed statistically significant differences between the 2 groups, β exhibited a better performance than the other 2 parameters, including ADC (or D).
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Affiliation(s)
- Y Sui
- From the Center for MR Research (Y.S., Y.X., M.M.K., X.J.Z.) Departments of Bioengineering (Y.S., X.J.Z.)
| | - Y Xiong
- From the Center for MR Research (Y.S., Y.X., M.M.K., X.J.Z.) Department of Radiology (Y.X., J.J., W.Z.), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - J Jiang
- Department of Radiology (Y.X., J.J., W.Z.), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - M M Karaman
- From the Center for MR Research (Y.S., Y.X., M.M.K., X.J.Z.)
| | | | - W Zhu
- Department of Radiology (Y.X., J.J., W.Z.), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - X J Zhou
- From the Center for MR Research (Y.S., Y.X., M.M.K., X.J.Z.) Departments of Bioengineering (Y.S., X.J.Z.) Radiology (K.L.X., X.J.Z.) Neurosurgery (X.J.Z.), University of Illinois at Chicago, Chicago, Illinois
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Jiang DZ, Zhong Y, Zhou DY, Wu WQ, Wu GY, Quan H. Application of brain multi-b-value diffusion-weighted imaging (DWI) in adolescent orphans from AIDS families. Br J Radiol 2016; 89:20150732. [PMID: 26892165 DOI: 10.1259/bjr.20150732] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To evaluate the application value of multi-b-value diffusion-weighted imaging (DWI) with mono-exponential model and stretched-exponential model in the diagnosis of HIV-positive patients. METHODS Multi-b-value (0, 50, 150, 200, 400, 600, 800 s mm(-2)) DWI was performed in 23 adolescent orphans from AIDS families, including 15 HIV-positive subjects and 8 HIV-negative healthy subjects. Apparent diffusion coefficient (ADC) values were fitted by mono-exponential model; distribution diffusion coefficient (DDC) values and heterogeneity index (α) values were fitted by stretched-exponential model in bilateral basal ganglia, then non-parametric tests were performed. RESULTS The signal intensity attenuation in multi-b-value DWI could be well described by both mono-exponential model and stretched-exponential model. In the left basal ganglia, mean α-values in HIV-positive subjects (α = 0.848 ± 0.068) were significantly lower than that in healthy subjects (α = 0.923 ± 0.050, p = 0.013). There was no statistical difference of α-values between HIV-positive subjects and healthy control subjects in the right basal ganglia. Apart from these, there were also no statistical differences of DDC values or ADC values between two groups in bilateral basal ganglia (all p > 0.05). In bilateral basal ganglia, DDC values were positively correlated with ADC values in HIV-positive patients (right basal ganglia: r = 0.832, p = 0.000; left basal ganglia: r = 0.770, p = 0.001) as well as in healthy cases (right basal ganglia: r = 0.927, p = 0.001; left basal ganglia: r = 0.878, p = 0.004). Receiver operating characteristic (ROC) curve analysis yielded area under the ROC curve (Az) values of 0.817 (p = 0.014 < 0.05) in the left basal ganglia. The sensitivity and specificity were 62.5% and 86.7%, respectively. CONCLUSION Through the study of asymptomatic HIV-positive subjects when b < 1000 s mm(-2), we can see stretched-exponential model DWI can provide more information than mono-exponential model DWI. ADVANCES IN KNOWLEDGE Multi-b-value DWI was performed in subjects with HIV. DWI measurements could be neuroimaging biomarkers of cerebral injury in the course of HIV infection.
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Affiliation(s)
- Da-Zhen Jiang
- 1 Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Yang Zhong
- 1 Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Ding-Yi Zhou
- 1 Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Wei-Qing Wu
- 1 Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Guang-Yao Wu
- 2 Medical Imaging Department of the Zhongnan Hospital, Wuhan University, Wuhan, China
| | - Hong Quan
- 1 Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
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Karaman MM, Sui Y, Wang H, Magin RL, Li Y, Zhou XJ. Differentiating low- and high-grade pediatric brain tumors using a continuous-time random-walk diffusion model at high b-values. Magn Reson Med 2015; 76:1149-57. [PMID: 26519663 DOI: 10.1002/mrm.26012] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 09/08/2015] [Accepted: 09/15/2015] [Indexed: 12/15/2022]
Abstract
PURPOSE To demonstrate that a continuous-time random-walk (CTRW) diffusion model can improve diagnostic accuracy of differentiating low- and high-grade pediatric brain tumors. METHODS Fifty-four children with histopathologically confirmed brain tumors underwent diffusion MRI scans at 3Twith 12 b-values (0-4000 s/mm(2) ). The diffusion imageswere fit to a simplified CTRW model to extract anomalous diffusion coefficient, Dm , and temporal and spatial heterogeneity parameters, α and β, respectively. Using histopathology results as reference, a k-means clustering algorithm and a receiver operating characteristic (ROC) analysis were employed to determine the sensitivity, specificity, and diagnostic accuracy of the CTRW parameters in differentiating tumor grades. RESULTS Significant differences between the low- and high-grade tumors were observed in the CTRW parameters (p-values<0.001). The k-means analysis showed that the combination of three CTRW parameters produced higher diagnostic accuracy (85% vs. 75%) and specificity (83% vs. 54%) than the apparent diffusion coefficient (ADC) from a mono-exponential model. The ROC analysis revealed that any combination of the CTRW parameters gave a larger area under the curve (0.90-0.96) than using ADC (0.80). CONCLUSION With its sensitivity to intravoxel heterogeneity, the simplified CTRW model is useful for non-invasive grading of pediatric brain tumors, particularly when surgical biopsy is not feasible. Magn Reson Med 76:1149-1157, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- M Muge Karaman
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Yi Sui
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - He Wang
- Philips Research China, Shanghai, China
| | - Richard L Magin
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Yuhua Li
- Xinhua Hospital, Shanghai, China
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA. .,Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA. .,Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA. .,Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA.
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Yan R, Haopeng P, Xiaoyuan F, Jinsong W, Jiawen Z, Chengjun Y, Tianming Q, Ji X, Mao S, Yueyue D, Yong Z, Jianfeng L, Zhenwei Y. Non-Gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index. Neuroradiology 2015; 58:121-32. [PMID: 26494463 DOI: 10.1007/s00234-015-1606-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2015] [Accepted: 10/02/2015] [Indexed: 12/12/2022]
Abstract
INTRODUCTION This study was conducted to compare the association of Gaussian and non-Gaussian magnetic resonance imaging (MRI)-derived parameters with histologic grade and MIB-1 (Ki-67 labeling) index (MI) in brain glioma. METHODS Sixty-five patients with pathologically confirmed glioma, who underwent diffusion-weighted MRI with 2 b values (0, 1000 s/mm(2)) and 22 b values (≤5000 s/mm(2)), respectively, were divided into three groups of grade II (n = 35), grade III (n = 8), and grade IV (n = 22). Comparisons by two groups were made for apparent diffusion coefficient (ADC), slow diffusion coefficient (Dslow), distributed diffusion coefficient (DDC), and heterogeneity index α. Analyses of receiver operating characteristic (ROC) curve were performed to maximize the area under the curve (AUC) for differentiating grade III + IV (high-grade glioma, HGG) from grade II (low-grade glioma, LGG) and grade IV (glioblastoma multiforme, GBM) from grade II + III (other grade glioma, OGG). Correlations with MI were analyzed for the MRI parameters. RESULTS On tumor regions, the values of ADC, Dslow, DDC, and α were significantly higher in grade II [(1.37 ± 0.29, 0.70 ± 0.11, 1.39 ± 0.34) (×10(-3) mm(2)/s) and 0.88 ± 0.05, respectively] than in grade III [(0.99 ± 0.13, 0.55 ± 0.07, 1.04 ± 0.20) (×10(-3) mm(2)/s) and 0.80 ± 0.03, respectively] and grade IV [(1.03 ± 0.14, 0.50 ± 0.05, 1.02 ± 0.16) (×10(-3) mm(2)/s) and 0.76 ± 0.04, respectively] (all P < 0.001). The parameter α showed the highest AUCs of 0.950 and 0.922 in discriminating HGG from LGG and GBM from OGG, respectively. Significant correlations with histologic grade and MI were observed for the MRI parameters. CONCLUSION The non-Gaussian MRI-derived parameters α and Dslow are superior to ADC in glioma grading, which are comparable with ADC as reliable biomarkers in noninvasively predicting the proliferation level of glioma malignancy.
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Affiliation(s)
- Ren Yan
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China
| | - Pang Haopeng
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China
| | - Feng Xiaoyuan
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China.
| | - Wu Jinsong
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Zhang Jiawen
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China
| | - Yao Chengjun
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Qiu Tianming
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Xiong Ji
- Department of Neuropathology, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Sheng Mao
- Department of Imaging, Suzhou Children's Hospital, Suzhou, Jiangsu, PR China
| | - Ding Yueyue
- Department of Imaging, Suzhou Children's Hospital, Suzhou, Jiangsu, PR China
| | - Zhang Yong
- MR Research, GE Healthcare, Shanghai, PR China
| | - Luo Jianfeng
- Department of Biostatistics, Public Health School, Fudan University, Shanghai, PR China
| | - Yao Zhenwei
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China
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Bai Y, Lin Y, Tian J, Shi D, Cheng J, Haacke EM, Hong X, Ma B, Zhou J, Wang M. Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging. Radiology 2015; 278:496-504. [PMID: 26230975 DOI: 10.1148/radiol.2015142173] [Citation(s) in RCA: 167] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
PURPOSE To quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas. MATERIALS AND METHODS This study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo-ADC, and perfusion fraction were calculated from diffusion-weighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations. RESULTS ADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P < .05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P < .05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P < .05). CONCLUSION Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.
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Affiliation(s)
- Yan Bai
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
| | - Yusong Lin
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
| | - Jie Tian
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
| | - Dapeng Shi
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
| | - Jingliang Cheng
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
| | - E Mark Haacke
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
| | - Xiaohua Hong
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
| | - Bo Ma
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
| | - Jinyuan Zhou
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
| | - Meiyun Wang
- From the Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan 450003, China (Y.B., D.S., B.M., M.W.); Software Technology School of Zhengzhou University (Y.L.); Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); Division of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China (J.C.); Department of Radiology, Wayne State University, Detroit, Mich (E.M.H.); Magnetic Resonance Innovations, Detroit, Mich (E.M.H.); and Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Md (X.H., B.M., J.Z., M.W.)
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Sui Y, Wang H, Liu G, Damen FW, Wanamaker C, Li Y, Zhou XJ. Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model. Radiology 2015; 277:489-96. [PMID: 26035586 DOI: 10.1148/radiol.2015142156] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
PURPOSE To demonstrate that a new set of parameters (D, β, and μ) from a fractional order calculus (FROC) diffusion model can be used to improve the accuracy of MR imaging for differentiating among low- and high-grade pediatric brain tumors. MATERIALS AND METHODS The institutional review board of the performing hospital approved this study, and written informed consent was obtained from the legal guardians of pediatric patients. Multi-b-value diffusion-weighted magnetic resonance (MR) imaging was performed in 67 pediatric patients with brain tumors. Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity), and a microstructural quantity μ were calculated by fitting the multi-b-value diffusion-weighted images to an FROC model. D, β, and μ values were measured in solid tumor regions, as well as in normal-appearing gray matter as a control. These values were compared between the low- and high-grade tumor groups by using the Mann-Whitney U test. The performance of FROC parameters for differentiating among patient groups was evaluated with receiver operating characteristic (ROC) analysis. RESULTS None of the FROC parameters exhibited significant differences in normal-appearing gray matter (P ≥ .24), but all showed a significant difference (P < .002) between low- (D, 1.53 μm(2)/msec ± 0.47; β, 0.87 ± 0.06; μ, 8.67 μm ± 0.95) and high-grade (D, 0.86 μm(2)/msec ± 0.23; β, 0.73 ± 0.06; μ, 7.8 μm ± 0.70) brain tumor groups. The combination of D and β produced the largest area under the ROC curve (0.962) in the ROC analysis compared with individual parameters (β, 0.943; D,0.910; and μ, 0.763), indicating an improved performance for tumor differentiation. CONCLUSION The FROC parameters can be used to differentiate between low- and high-grade pediatric brain tumor groups. The combination of FROC parameters or individual parameters may serve as in vivo, noninvasive, and quantitative imaging markers for classifying pediatric brain tumors.
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Affiliation(s)
- Yi Sui
- From the Center for MR Research (Y.S., G.L., F.W.D., X.J.Z.) and Departments of Radiology (C.W., X.J.Z.), Neurosurgery (X.J.Z.), and Bioengineering (Y.S., X.J.Z.), University of Illinois Hospital & Health Sciences System, 1801 W Taylor St, MC-707, Suite 1A, Chicago, IL 60612; Applied Science Laboratory, GE Healthcare, Shanghai, China (H.W.); and Department of Radiology, Xinhua Hospital, Shanghai, China (Y.L.)
| | - He Wang
- From the Center for MR Research (Y.S., G.L., F.W.D., X.J.Z.) and Departments of Radiology (C.W., X.J.Z.), Neurosurgery (X.J.Z.), and Bioengineering (Y.S., X.J.Z.), University of Illinois Hospital & Health Sciences System, 1801 W Taylor St, MC-707, Suite 1A, Chicago, IL 60612; Applied Science Laboratory, GE Healthcare, Shanghai, China (H.W.); and Department of Radiology, Xinhua Hospital, Shanghai, China (Y.L.)
| | - Guanzhong Liu
- From the Center for MR Research (Y.S., G.L., F.W.D., X.J.Z.) and Departments of Radiology (C.W., X.J.Z.), Neurosurgery (X.J.Z.), and Bioengineering (Y.S., X.J.Z.), University of Illinois Hospital & Health Sciences System, 1801 W Taylor St, MC-707, Suite 1A, Chicago, IL 60612; Applied Science Laboratory, GE Healthcare, Shanghai, China (H.W.); and Department of Radiology, Xinhua Hospital, Shanghai, China (Y.L.)
| | - Frederick W Damen
- From the Center for MR Research (Y.S., G.L., F.W.D., X.J.Z.) and Departments of Radiology (C.W., X.J.Z.), Neurosurgery (X.J.Z.), and Bioengineering (Y.S., X.J.Z.), University of Illinois Hospital & Health Sciences System, 1801 W Taylor St, MC-707, Suite 1A, Chicago, IL 60612; Applied Science Laboratory, GE Healthcare, Shanghai, China (H.W.); and Department of Radiology, Xinhua Hospital, Shanghai, China (Y.L.)
| | - Christian Wanamaker
- From the Center for MR Research (Y.S., G.L., F.W.D., X.J.Z.) and Departments of Radiology (C.W., X.J.Z.), Neurosurgery (X.J.Z.), and Bioengineering (Y.S., X.J.Z.), University of Illinois Hospital & Health Sciences System, 1801 W Taylor St, MC-707, Suite 1A, Chicago, IL 60612; Applied Science Laboratory, GE Healthcare, Shanghai, China (H.W.); and Department of Radiology, Xinhua Hospital, Shanghai, China (Y.L.)
| | - Yuhua Li
- From the Center for MR Research (Y.S., G.L., F.W.D., X.J.Z.) and Departments of Radiology (C.W., X.J.Z.), Neurosurgery (X.J.Z.), and Bioengineering (Y.S., X.J.Z.), University of Illinois Hospital & Health Sciences System, 1801 W Taylor St, MC-707, Suite 1A, Chicago, IL 60612; Applied Science Laboratory, GE Healthcare, Shanghai, China (H.W.); and Department of Radiology, Xinhua Hospital, Shanghai, China (Y.L.)
| | - Xiaohong Joe Zhou
- From the Center for MR Research (Y.S., G.L., F.W.D., X.J.Z.) and Departments of Radiology (C.W., X.J.Z.), Neurosurgery (X.J.Z.), and Bioengineering (Y.S., X.J.Z.), University of Illinois Hospital & Health Sciences System, 1801 W Taylor St, MC-707, Suite 1A, Chicago, IL 60612; Applied Science Laboratory, GE Healthcare, Shanghai, China (H.W.); and Department of Radiology, Xinhua Hospital, Shanghai, China (Y.L.)
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Schmainda KM. Diffusion-weighted MRI as a biomarker for treatment response in glioma. CNS Oncol 2015; 1:169-80. [PMID: 23936625 DOI: 10.2217/cns.12.25] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Diffusion-weighted imaging (DWI) is a powerful MRI method, which probes abnormalities of tissue structure by detecting microscopic changes in water mobility at a cellular level beyond what is available with other imaging techniques. Accordingly, DWI has the potential to identify pathology before gross anatomic changes are evident on standard anatomical brain images. These features of tissue characterization and earlier detection are what make DWI particularly appealing for the evaluation of gliomas and the newer therapies where standard anatomical imaging is proving insufficient. This article focuses on the basic principles and applications of DWI, and its derived parameter, the apparent diffusion coefficient, for the purposes of diagnosis and evaluation of glioma, especially in the context of monitoring response to therapy.
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Bano S, Waraich MM, Khan MA, Buzdar SA, Manzur S. Diagnostic value of apparent diffusion coefficient for the accurate assessment and differentiation of intracranial meningiomas. Acta Radiol Short Rep 2013; 2:2047981613512484. [PMID: 24349716 PMCID: PMC3863968 DOI: 10.1177/2047981613512484] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Accepted: 10/16/2013] [Indexed: 11/30/2022] Open
Abstract
Background Diffusion-weighted imaging (DWI) along with the calculation of apparent diffusion coefficient (ADC), is a novel, non-invasive, and reliable technique of choice for accurate assessment and for the treatment planning of different types of brain tumors. It is more advantageous in the distinction and differentiation of benign from malignant meningiomas on the basis of ADC values. Purpose To investigate the utility of DW magnetic resonance imaging (MRI), and to compare the apparent diffusion coefficient (ADC) obtained at two b-values for an authentic and preoperative characterization of meningiomas. Material and Methods Twenty-six patients with clinically diagnosed or histologically verified meningioma (18 benign and 8 malignant) underwent imaging including DWI at 1.5 T. DW images were obtained at b = 1000 s/mm2 and b = 2000 s/mm2, ADC maps were generated at both the b-values. Signal intensities (SIs) and ADCs for solid tumorous tissues, contralateral normal tissues, and peritumoral edema were calculated and normalized ADC (NADC) ratio were determined for tumorous tissues. SI scores, ADC maps, and ADC values were analyzed visually and quantitatively, and were compared at both the b-values. Results DW images at b = 2000 s/mm2 were more conspicuity (either hyperintense or hypointense) with improved contrast. The mean ADC of malignant meningiomas (0.64 ± 0.05 and 0.42 ± 0.03) was significantly lower (P < 0.05) as compared with benign meningiomas (1.04 ± 0.12 and 0.80 ± 0.07) at both the b-values. Mean NADC ratio in the malignant type was also significantly lower (P < 0.05) than the benign type at both the b-values. Mean ADC values for peritumoral edema do not differ between benign and malignant meningiomas. Conclusion 1.5-T DWI using high b-values improved our ability to differentiate benign from malignant meningiomas. DWI may play an important role in the preoperative radiological evaluation and the recognition of these types for proper surgical treatment.
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Affiliation(s)
- Shazia Bano
- Department of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | | | - Muhammad Afzal Khan
- Department of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Saeed Ahmad Buzdar
- Department of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Shahid Manzur
- Diagnostic Radiology, Quaid-e-Azam Medical College B.V. Hospital, Bahawalpur, Pakistan
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Paudyal R, Bagher-Ebadian H, Nagaraja TN, Fenstermacher JD, Ewing JR. Modeling of Look-Locker estimates of the magnetic resonance imaging estimate of longitudinal relaxation rate in tissue after contrast administration. Magn Reson Med 2011; 66:1432-44. [PMID: 21630341 DOI: 10.1002/mrm.22852] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2010] [Revised: 12/07/2010] [Accepted: 01/05/2011] [Indexed: 11/08/2022]
Abstract
This paper models the behavior of the longitudinal relaxation rate of the protons of tissue water R(1) (R(1) = 1/T(1) ), measured in a Look-Locker experiment at 7 Tesla after administration of a paramagnetic contrast agent (CA). It solves the Bloch-McConnell equations for the longitudinal magnetization of the protons of water in a three-site two-exchange (3S2X) model with boundary conditions appropriate to repeated sampling of magnetization. The extent to which equilibrium intercompartmental water exchange kinetics affect monoexponential estimates of R(1) after administration of a CA in dynamic contrast enhanced experiment is described. The relation between R(1) and tissue CA concentration was calculated for CA restricted to the intravascular, or to the intravascular and extracellular compartments, by varying model parameters to mimic experimental data acquired in a rat model of cerebral tumor. The model described a nearly linear relationship between R(1) and tissue concentration of CA, but demonstrated that the apparent longitudinal relaxivity of CA depends upon tissue type. The practical consequence of this finding is that the extended Patlak plot linearizes the ΔR(1) data in tissue with leaky microvessels, accurately determines the influx rate of the CA across these microvessels, but underestimates the volume of intravascular blood water.
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Affiliation(s)
- Ramesh Paudyal
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan 48202, USA
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Gillies RJ, Anderson AR, Gatenby RA, Morse DL. The biology underlying molecular imaging in oncology: from genome to anatome and back again. Clin Radiol 2010; 65:517-21. [PMID: 20541651 DOI: 10.1016/j.crad.2010.04.005] [Citation(s) in RCA: 94] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2009] [Revised: 04/23/2010] [Accepted: 04/30/2010] [Indexed: 01/03/2023]
Abstract
Cancers are complex, evolving, multiscale ecosystems that are characterized by profound spatial and temporal heterogeneity. The interactions in cancer are non-linear in that small changes in one variable can have large changes on another. These multiple interacting phenotypes and spatial scales can best be understood with appropriate mathematical and computational models. Imaging is central to this investigation because it can non-destructively and longitudinally characterize spatial variations in the tumour phenotype and environment so that the system dynamics over time can be captured quantitatively.
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Affiliation(s)
- R J Gillies
- H Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33602, USA.
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Hoff BA, Chenevert TL, Bhojani MS, Kwee TC, Rehemtulla A, Le Bihan D, Ross BD, Galbán CJ. Assessment of multiexponential diffusion features as MRI cancer therapy response metrics. Magn Reson Med 2010; 64:1499-509. [PMID: 20860004 DOI: 10.1002/mrm.22507] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2009] [Revised: 03/24/2010] [Accepted: 04/20/2010] [Indexed: 12/27/2022]
Abstract
The aim of this study was to empirically test the effect of chemotherapy-induced tissue changes in a glioma model as measured by several diffusion indices calculated from nonmonoexponential formalisms over a wide range of b-values. We also compared these results to the conventional two-point apparent diffusion coefficient calculation using nominal b-values. Diffusion-weighted imaging was performed over an extended range of b-values (120-4000 sec/mm(2) ) on intracerebral rat 9L gliomas before and after a single dose of 1,3-bis(2-chloroethyl)-1-nitrosourea. Diffusion indices from three formalisms of diffusion-weighted signal decay [(a) two-point analytical calculation using either low or high b-values, (b) a stretched exponential formalism, and (c) a biexponential fit] were tested for responsiveness to therapy-induced differences between control and treated groups. Diffusion indices sensitive to "fast diffusion" produced the largest response to treatment, which resulted in significant differences between groups. These trends were not observed for "slow diffusion" indices. Although the highest rate of response was observed from the biexponential formalism, this was not found to be significantly different from the conventional monoexponential apparent diffusion coefficient method. In conclusion, parameters from the more complicated nonmonoexponential formalisms did not provide additional sensitivity to treatment response in this glioma model beyond that observed from the two-point conventional monoexponential apparent diffusion coefficient method.
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Affiliation(s)
- Benjamin A Hoff
- Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan 48109-2200, USA
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Abstract
MRI offers a tremendous armamentarium of different methods that can be employed in brain tumor characterization. MR diffusion imaging has become a widely accepted method to probe for the presence of fluid pools and molecular tissue water mobility. For most clinical applications of diffusion imaging, it is assumed that the diffusion signal vs diffusion weighting factor b decays monoexponentially. Within this framework, the measurement of a single diffusion coefficient in brain tumors permits an approximate categorization of tumor type and, for some tumors, definitive diagnosis. In most brain tumors, when compared with normal brain tissue, the diffusion coefficient is elevated. The presence of peritumoral edema, which also exhibits an elevated diffusion coefficient, often precludes the delineation of the tumor on the basis of diffusion information alone. Serially obtained diffusion data are useful to document and even predict the cellular response to drug or radiation therapy. Diffusion measurements in tissues over an extended range of b factors have clearly shown that the monoparametric description of the MR diffusion signal decay is incomplete. Very high diffusion weighting on clinical systems requires substantial compromise in spatial resolution. However, after suitable analysis, superior separation of malignant brain tumors, peritumoral edema and normal brain tissue can be achieved. These findings are also discussed in the light of tissue-specific differences in membrane structure and the restrictions exerted by membranes on diffusion. Finally, measurement of the directional dependence of diffusion permits the assessment of white matter integrity and dislocation. Such information, particularly in conjunction with advanced post-processing, is considered to be immensely useful for therapy planning. Diffusion imaging, which permits monoexponential analysis and provides directional diffusion information, is performed routinely in brain tumor patients. More advanced methods require improvement in acquisition speed and spatial resolution to gain clinical acceptance.
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Affiliation(s)
- Stephan E Maier
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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Abstract
Advanced imaging provides insight into biophysical, physiologic, metabolic, or functional properties of tissues. Because water mobility is sensitive to cellular homeostasis, cellular density, and microstructural organization, it is considered a valuable tool in the advanced imaging arsenal. This article summarizes diffusion imaging concepts and highlights clinical applications of diffusion MR imaging for oncologic imaging. Diffusion tensor imaging and its derivative maps of diffusion anisotropy allow assessment of tumor compression or destruction of adjacent normal tissue anisotropy and may aid to assess tumor infiltration and aid presurgical planning.
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