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Huang Q, Wu J, Le N, Shen Y, Guo P, Schreck KC, Kamson D, Blair L, Heo HY, Li X, Li W, Sair HL, Blakeley JO, Laterra J, Holdhoff M, Grossman SA, Mukherjee D, Bettegowda C, van Zijl P, Zhou J, Jiang S. CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas. Magn Reson Imaging 2023; 102:222-228. [PMID: 37321378 PMCID: PMC10528251 DOI: 10.1016/j.mri.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023]
Abstract
New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence.
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Affiliation(s)
- Qianqi Huang
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jingpu Wu
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nhat Le
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yiqing Shen
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Pengfei Guo
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Karisa C Schreck
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David Kamson
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Lindsay Blair
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Hye-Young Heo
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Xu Li
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Wenbo Li
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Haris L Sair
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jaishri O Blakeley
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - John Laterra
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Matthias Holdhoff
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Stuart A Grossman
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Debraj Mukherjee
- Department of Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Chetan Bettegowda
- Department of Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Peter van Zijl
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jinyuan Zhou
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Shanshan Jiang
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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Chen K, Jiang XW, Deng LJ, She HL. Differentiation between glioma recurrence and treatment effects using amide proton transfer imaging: A mini-Bayesian bivariate meta-analysis. Front Oncol 2022; 12:852076. [PMID: 35978813 PMCID: PMC9376615 DOI: 10.3389/fonc.2022.852076] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 06/29/2022] [Indexed: 11/24/2022] Open
Abstract
Background Amide proton transfer (APT) imaging as an emerging MRI approach has been used for distinguishing tumor recurrence (TR) and treatment effects (TEs) in glioma patients, but the initial results from recent studies are different. Aim The aim of this study is to systematically review and quantify the diagnostic performance of APT in assessing treatment response in patients with post-treatment gliomas. Methods A systematic search in PubMed, EMBASE, and the Web of Science was performed to retrieve related original studies. For the single and added value of APT imaging in distinguishing TR from TEs, we calculated pooled sensitivity and specificity by using Bayesian bivariate meta-analyses. Results Six studies were included, five of which reported on single APT imaging parameters and four of which reported on multiparametric MRI combined with APT imaging parameters. For single APT imaging parameters, the pooled sensitivity and specificity were 0.85 (95% CI: 0.75–0.92) and 0.88 (95% CI: 0.74–0.97). For multiparametric MRI including APT, the pooled sensitivity and specificity were 0.92 (95% CI: 0.85–0.97) and 0.83 (95% CI: 0.55–0.97), respectively. In addition, in the three studies reported on both single and added value of APT imaging parameters, the combined imaging parameters further improved diagnostic performance, yielding pooled sensitivity and specificity of 0.91 (95% CI: 0.80–0.97) and 0.92 (95% CI: 0.79–0.98), respectively, but the pooled sensitivity was 0.81 (95% CI: 0.65-0.93) and specificity was 0.82 (95% CI: 0.61–0.94) for single APT imaging parameters. Conclusion APT imaging showed high diagnostic performance in assessing treatment response in patients with post-treatment gliomas, and the addition of APT imaging to other advanced MRI techniques can improve the diagnostic accuracy for distinguishing TR from TE.
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Affiliation(s)
- Kai Chen
- Department of Medical Imaging, Shenzhen Samii Medical Center, Shenzhen, China
| | - Xi-Wen Jiang
- Department of Medical Imaging, Affiliated Hospital of Xiangnan University (Clinical College), Chenzhou, China
| | - Li-jing Deng
- Department of Neonatology, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Hua-Long She
- Department of Medical Imaging, Affiliated Hospital of Xiangnan University (Clinical College), Chenzhou, China
- *Correspondence: Hua-Long She,
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