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Sheng L, Yuan E, Yuan F, Song B. Amide proton transfer-weighted imaging of the abdomen: Current progress and future directions. Magn Reson Imaging 2024; 107:88-99. [PMID: 38242255 DOI: 10.1016/j.mri.2024.01.006] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/13/2024] [Accepted: 01/14/2024] [Indexed: 01/21/2024]
Abstract
The chemical exchange saturation transfer technique serves as a valuable tool for generating in vivo image contrast based on the content of various proton groups, including amide protons, amine protons, and aliphatic protons. Among these, amide proton transfer-weighted (APTw) imaging has seen extensive development as a means to assess the biochemical status of lesions. The exchange from saturated amide protons to bulk water protons during and following the saturation ratio frequency pulse contributes to detectable APT signals. While APTw imaging has garnered significant attention in the central nervous system, demonstrating noteworthy findings in cerebral neoplasia, stroke, and Alzheimer's disease over the past decade, its application in the abdomen has been a relatively recent progression. Notably, studies have explored its utility in hepatocellular carcinoma, prostate cancer, and cervical carcinoma within the abdominal context. Despite these advancements, there is a paucity of reviews on APTw imaging in abdominal applications. This paper aims to fill this gap by providing a concise overview of the fundamental theories underpinning APTw imaging. Additionally, we systematically summarize its diverse clinical applications in the abdomen, with a particular focus on the digestive and urogenital systems. Finally, the manuscript concludes by discussing technical limitations and factors influencing APTw imaging in abdominal applications, along with prospects for future research.
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Affiliation(s)
- Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Enyu Yuan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Fang Yuan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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Avery JC, Knox S, Deslandes A, Leonardi M, Lo G, Wang H, Zhang Y, Holdsworth-Carson SJ, Thi Nguyen TT, Condous GS, Carneiro G, Hull ML. Noninvasive diagnostic imaging for endometriosis part 2: a systematic review of recent developments in magnetic resonance imaging, nuclear medicine and computed tomography. Fertil Steril 2024; 121:189-211. [PMID: 38110143 DOI: 10.1016/j.fertnstert.2023.12.017] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 12/20/2023]
Abstract
Endometriosis affects 1 in 9 women, taking 6.4 years to diagnose using conventional laparoscopy. Non-invasive imaging enables timelier diagnosis, reducing diagnostic delay, risk and expense of surgery. This review updates literature exploring the diagnostic value of specialist endometriosis magnetic resonance imaging (eMRI), nuclear medicine (NM) and computed tomography (CT). Searching after the 2016 IDEA consensus, 6192 publications were identified, with 27 studies focused on imaging for endometriosis. eMRI was the subject of 14 papers, NM and CT, 11, and artificial intelligence (AI) utilizing eMRI, 2. eMRI papers describe diagnostic accuracy for endometriosis, methodologies, and innovations. Advantages of eMRI include its: ability to diagnose endometriosis in those unable to tolerate transvaginal endometriosis ultrasound (eTVUS); a panoramic pelvic view, easy translation to surgical fields; identification of hyperintense iron in endometriotic lesions; and ability to identify super-pelvic lesions. Sequence standardization means eMRI is less operator-dependent than eTVUS, but higher costs limit its role to a secondary diagnostic modality. eMRI for deep and ovarian endometriosis has sensitivities of 91-93.5% and specificities of 86-87.5% making it reliable for surgical mapping and diagnosis. Superficial lesions too small for detection in larger capture sequences, means a negative eMRI doesn't exclude endometriosis. Combined with thin sequence capture and improved reader expertise, eMRI is poised for rapid adoption into clinical practice. NM labeling is diagnostically limited in absence of suitable unique marker for endometrial-like tissue. CT studies expose the reproductively aged to radiation. AI diagnostic tools, combining independent eMRI and eTVUS endometriosis markers, may result in powerful capability. Broader eMRI use, will optimize standards and protocols. Reporting systems correlating to surgical anatomy will facilitate interdisciplinary preoperative dialogues. eMRI endometriosis diagnosis should reduce repeat surgeries with mental and physical health benefits for patients. There is potential for early eMRI diagnoses to prevent chronic pain syndromes and protect fertility outcomes.
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Affiliation(s)
- Jodie C Avery
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
| | - Steven Knox
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Benson Radiology, Adelaide, Australia
| | - Alison Deslandes
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Mathew Leonardi
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Department of Obstetrics and Gynecology McMaster University, Hamilton, Canada
| | - Glen Lo
- Curtin University Medical School Perth, Australia
| | - Hu Wang
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Australian Institute for Machine Learning, University of Adelaide, Australia
| | - Yuan Zhang
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Australian Institute for Machine Learning, University of Adelaide, Australia
| | - Sarah Jane Holdsworth-Carson
- Julia Argyrou Endometriosis Centre, Epworth HealthCare, Richmond, Australia; Department of Obstetrics and Gynaecology, University of Melbourne, Parkville, Australia
| | - Tran Tuyet Thi Nguyen
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Embrace Fertility, Adelaide, Australia
| | - George Stanley Condous
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Omni Ultrasound and Gynaecological Care, Sydney Australia, (j)Department of Obstetrics and Gynaecology, University of Melbourne, Parkville, Australia
| | - Gustavo Carneiro
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; University of Surrey, Guildford, United Kingdom
| | - Mary Louise Hull
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Embrace Fertility, Adelaide, Australia
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Yu Y, Song X, Zeng Z, Wang L, Zhang L, Zhao H, Zheng Z. Amide proton transfer weighted MRI in differential diagnosis of ovarian masses with cystic components: A preliminary study. Magn Reson Imaging 2023; 103:216-223. [PMID: 37517767 DOI: 10.1016/j.mri.2023.07.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/01/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate the performance of three-dimensional (3D) amide proton transfer-weighted (APTw) MRI in the differentiation between benign and malignant ovarian masses based on single-slice and all-slice analysis of cystic regions. MATERIALS AND METHODS Patients were consecutively recruited and underwent conventional pelvic MRI and APTw MRI. Two radiologists independently assessed ovarian masses blinded to the histopathological results. Three APTw SI values were generated from the cystic regions of the masses: (1) APTw SI of a single representative slice (RS); (2) average (AVE) of APTw SIs of all slices of the mass; (3) area-weighted (AW) average of APTw SIs of all slices of the mass. O-RADS MRI score of each mass was reported. Independent sample t-test and receiver operating characteristic (ROC) curve analysis were performed for comparison. Inter- and intra-observer reliability were assessed by the intraclass correlation coefficient (ICC) and quadratic kappa coefficient. RESULTS 46 ovarian masses were included for final analysis. The three APTw SI values were higher in cystic regions of malignant ovarian masses compared with benign lesions (p<0.0001). ROC curve analysis showed no significant difference in diagnostic performance among three APTw SI values and the O-RADS MRI score (AUC: RS-APTw SI, 0.930; AVE-APTw SI, 0.927; AW-APTw SI, 0.935; O-RADS score, 0.937). CONCLUSIONS APTw MRI may be used as a noninvasive tool for the differentiation of benign and malignant ovarian masses based on the analysis of the cystic regions.
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Affiliation(s)
- Yibei Yu
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing 102218, China
| | - Xiaolei Song
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing 100084, China
| | - Zhen Zeng
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing 102218, China
| | - Lixue Wang
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing 102218, China
| | - Lei Zhang
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing 102218, China
| | - Hongliang Zhao
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing 102218, China
| | - Zhuozhao Zheng
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing 102218, China.
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