1
|
Xu Y, Wan Q, Ren X, Jiang Y, Yao J, Wu P, Shen A, Wang P. Feasibility of Amide Proton Transfer-Weighted Imaging at 3T for Renal Masses: A Preliminary Study. Curr Med Imaging 2024; 20:CMIR-EPUB-139655. [PMID: 38591213 DOI: 10.2174/0115734056284650240325044027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/08/2024] [Accepted: 02/19/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND To investigate the optimal B1,rms value of renal amide proton transfer-weighted (APTw) images and the reproducibility of this value, and to explore the utility of APT imaging of renal masses and kidney tissues. METHODS APTw images with different B1,rms values were repeatedly recorded in 15 healthy volunteers to determine the optimal value. Two 4-point Likert scales (poor [1] to excellent [4]) were used to evaluate contour clarity and artifacts in masses and normal tissues. The APTw values of masses and normal tissues were then compared in evaluable images (contour clarity score > 1, artifacts score > 1). The APTw of malignant masses, normal tissues, and benign masses were calculated and compared with the Mann-Whitney U test. RESULTS The optimal scanning parameter of B1,rms was 2 μT, and the APTw images had good agreement in the volunteers. Our study of APTw imaging examined 70 renal masses (13 benign, 57 malignant) and 49 normal kidneys (including those from 15 healthy volunteers). The mean APTw value for renal malignant masses (2.28(1.55)) was different from that for benign masses (0.91(1.30)) (P<0.001), renal cortex (1.30 (1.25)) (P<0.001), renal medulla (1.64 (1.33)) (P<0.05), and renal pelvis (5.49 (2.65)) (P<0.001). CONCLUSION These preliminary data demonstrate that APTw imaging of the kidneys has potential use as an imaging biomarker for the differentiation of normal tissues, malignant masses, and benign masses.
Collapse
Affiliation(s)
- Yun Xu
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Qingxuan Wan
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xihui Ren
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Yutao Jiang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Jing Yao
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Peng Wu
- Philips Healthcare, Shanghai, 200072, China
| | - Aijun Shen
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| |
Collapse
|