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.

Humans Protons Brain Neoplasms / diagnostic imaging Amides Neoplasm Recurrence, Local / diagnostic imaging Glioma / diagnostic imaging Magnetic Resonance Imaging / methods
Amide proton transfer-weighted imaging Arterial spin labeling imaging Glioblastoma Magnetic resonance imaging Treatment effect

Journal

Magnetic resonance imaging
ISSN: 1873-5894
Titre abrégé: Magn Reson Imaging
Pays: Netherlands
ID NLM: 8214883

Informations de publication

Date de publication:
10 2023
Historique:
received: 02 03 2023
accepted: 12 06 2023
pmc-release: 01 10 2024
medline: 31 7 2023
pubmed: 16 6 2023
entrez: 15 6 2023
Statut: ppublish

Résumé

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.

Identifiants

DOI: 10.1016/j.mri.2023.06.003 PMID: 37321378 PMC: PMC10528251
pubmed: 37321378
pii: S0730-725X(23)00101-7
doi: 10.1016/j.mri.2023.06.003
pmc: PMC10528251
mid: NIHMS1910696
pii:
doi:

Substances chimiques

Protons 0
Amides 0

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

222-228

Subventions

Organisme : NICHD NIH HHS
ID : P50 HD103538
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA228188
Pays : United States
Organisme : NCI NIH HHS
ID : R37 CA248077
Pays : United States
Organisme : NIH HHS
ID : S10 OD021648
Pays : United States

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

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Auteurs

Qianqi Huang (Q)

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 (J)

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 (N)

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 (Y)

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 (P)

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 (KC)

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 (D)

Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

Lindsay Blair (L)

Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

Hye-Young Heo (HY)

Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

Xu Li (X)

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 (W)

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 (HL)

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 (JO)

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 (J)

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 (M)

Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

Stuart A Grossman (SA)

Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

Debraj Mukherjee (D)

Department of Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

Chetan Bettegowda (C)

Department of Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

Peter van Zijl (P)

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 (J)

Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

Shanshan Jiang (S)

Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA. Electronic address: sjiang21@jhmi.edu.

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