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Wang C, He N, Zhang Y, Li Y, Huang P, Liu Y, Jin Z, Cheng Z, Liu Y, Wang Y, Zhang C, Haacke EM, Chen S, Yan F, Yang G. Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging. J Magn Reson Imaging 2024; 60:1904-1915. [PMID: 38236577 DOI: 10.1002/jmri.29245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. PURPOSE To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. STUDY TYPE Prospective. POPULATION/SUBJECTS 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. FIELD STRENGTH/SEQUENCE 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. ASSESSMENT A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. STATISTICAL TESTS We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. RESULTS NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. DATA CONCLUSION Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Chenglong Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Liu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA
| | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
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Quattrone A, Zappia M, Quattrone A. Simple biomarkers to distinguish Parkinson's disease from its mimics in clinical practice: a comprehensive review and future directions. Front Neurol 2024; 15:1460576. [PMID: 39364423 PMCID: PMC11446779 DOI: 10.3389/fneur.2024.1460576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2024] [Accepted: 09/09/2024] [Indexed: 10/05/2024] Open
Abstract
In the last few years, a plethora of biomarkers have been proposed for the differentiation of Parkinson's disease (PD) from its mimics. Most of them consist of complex measures, often based on expensive technology, not easily employed outside research centers. MRI measures have been widely used to differentiate between PD and other parkinsonism. However, these measurements were often performed manually on small brain areas in small patient cohorts with intra- and inter-rater variability. The aim of the current review is to provide a comprehensive and updated overview of the literature on biomarkers commonly used to differentiate PD from its mimics (including parkinsonism and tremor syndromes), focusing on parameters derived by simple qualitative or quantitative measurements that can be used in routine practice. Several electrophysiological, sonographic and MRI biomarkers have shown promising results, including the blink-reflex recovery cycle, tremor analysis, sonographic or MRI assessment of substantia nigra, and several qualitative MRI signs or simple linear measures to be directly performed on MR images. The most significant issue is that most studies have been conducted on small patient cohorts from a single center, with limited reproducibility of the findings. Future studies should be carried out on larger international cohorts of patients to ensure generalizability. Moreover, research on simple biomarkers should seek measurements to differentiate patients with different diseases but similar clinical phenotypes, distinguish subtypes of the same disease, assess disease progression, and correlate biomarkers with pathological data. An even more important goal would be to predict the disease in the preclinical phase.
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Affiliation(s)
- Andrea Quattrone
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Mario Zappia
- Department of Medical, Surgical Sciences and Advanced Technologies, GF Ingrassia, University of Catania, Catania, Italy
| | - Aldo Quattrone
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
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Guan X, Lancione M, Ayton S, Dusek P, Langkammer C, Zhang M. Neuroimaging of Parkinson's disease by quantitative susceptibility mapping. Neuroimage 2024; 289:120547. [PMID: 38373677 DOI: 10.1016/j.neuroimage.2024.120547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 02/02/2024] [Accepted: 02/17/2024] [Indexed: 02/21/2024] Open
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Marta Lancione
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Scott Ayton
- Florey Institute, The University of Melbourne, Australia
| | - Petr Dusek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czechia; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Auenbruggerplatz 22, Prague 8036, Czechia
| | | | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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Sung YH, Kim JS, Yoo SW, Shin NY, Nam Y, Ahn TB, Yoo D, Lee KM, Kim HG, Koh SB, Kim J, Kim I, Kwon DY, Lee Y, Kim C, Chung SJ, Jo S, Lee SH, Kim SJ, Kim M, Lyoo CH, Baek MS, Kang SY, Chang SK, Jo SW, Lee SA, Ma HI, Kim YE, Kim ES, Kim YJ, Kim HS, Woo MH, Choi HJ, Kim EY. A prospective multi-centre study of susceptibility map-weighted MRI for the diagnosis of neurodegenerative parkinsonism. Eur Radiol 2022; 32:3597-3608. [PMID: 35064313 DOI: 10.1007/s00330-021-08454-z] [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: 07/18/2021] [Revised: 09/25/2021] [Accepted: 10/27/2021] [Indexed: 11/04/2022]
Abstract
OBJECTIVES This study aimed to compare susceptibility map-weighted imaging (SMwI) using various MRI machines (three vendors) with N-3-fluoropropyl-2-β-carbomethoxy-3-β-(4-iodophe nyl)nortropane (18F-FP-CIT) PET in the diagnosis of neurodegenerative parkinsonism in a multi-centre setting. METHODS We prospectively recruited 257 subjects, including 157 patients with neurodegenerative parkinsonism, 54 patients with non-neurodegenerative parkinsonism, and 46 healthy subjects from 10 hospitals between November 2019 and October 2020. All participants underwent both SMwI and 18F-FP-CIT PET. SMwI was interpreted by two independent reviewers for the presence or absence of abnormalities in nigrosome 1, and discrepancies were resolved by consensus. 18F-FP-CIT PET was used as the reference standard. Inter-observer agreement was tested using Cohen's kappa coefficient. McNemar's test was used to test the agreement between the interpretations of SMwI and 18F-FP-CIT PET per participant and substantia nigra (SN). RESULTS The inter-observer agreement was 0.924 and 0.942 per SN and participant, respectively. The diagnostic sensitivity of SMwI was 97.9% and 99.4% per SN and participant, respectively; its specificity was 95.9% and 95.2%, respectively, and its accuracy was 97.1% and 97.7%, respectively. There was no significant difference between the results of SMwI and 18F-FP-CIT PET (p > 0.05, for both SN and participant). CONCLUSIONS This study demonstrated that the high diagnostic performance of SMwI was maintained in a multi-centre setting with various MRI scanners, suggesting the generalisability of SMwI for determining nigrostriatal degeneration in patients with parkinsonism. KEY POINTS • Susceptibility map-weighted imaging helps clinicians to predict nigrostriatal degeneration. • The protocol for susceptibility map-weighted imaging can be standardised across MRI vendors. • Susceptibility map-weighted imaging showed diagnostic performance comparable to that of dopamine transporter PET in a multi-centre setting with various MRI scanners.
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Affiliation(s)
- Young Hee Sung
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea
| | - Joong-Seok Kim
- Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sang-Won Yoo
- Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Na-Young Shin
- Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yoonho Nam
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Republic of Korea
| | - Tae-Beom Ahn
- Department of Neurology, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Dallah Yoo
- Department of Neurology, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Kyung Mi Lee
- Department of Radiology, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Hyug-Gi Kim
- Department of Radiology, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Seong-Beom Koh
- Department of Neurology and Parkinson's Disease Centre, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jinhee Kim
- Department of Neurology and Parkinson's Disease Centre, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Ilsoo Kim
- Department of Neurology and Parkinson's Disease Centre, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Do-Young Kwon
- Department of Neurology, Center for Movement Disorders and Neurodegenerative Diseases, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, Republic of Korea
| | - Younghen Lee
- Department of Radiology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, Republic of Korea
| | - Chulhan Kim
- Department of Nuclear Medicine, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, Republic of Korea
| | - Sun Ju Chung
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sungyang Jo
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung Hyun Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Minjae Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Min Seok Baek
- Department of Neurology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Gangwon do, Wonju, Republic of Korea
| | - Suk Yun Kang
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Suk Ki Chang
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Sang-Won Jo
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Seun Ah Lee
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Hyeo-Il Ma
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang-si, Gyeonggi-do, Republic of Korea
| | - Young Eun Kim
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang-si, Gyeonggi-do, Republic of Korea
| | - Eun Soo Kim
- Department of Radiology, Hallym University Sacred Heart Hospital, Anyang-si, Gyeonggi-do, Republic of Korea
| | - Yun Joong Kim
- Department of Neurology, Center for Neurodegenerative Disorders, Yongin Severance Hospital, Yonsei University Health System, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea
| | - Hyun Sook Kim
- Department of Neurology, CHA Bundang Medical Center, CHA University, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Min-Hee Woo
- Department of Neurology, CHA Bundang Medical Center, CHA University, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Hye Jeong Choi
- Radiology Department, CHA Bundang Medical Center, CHA University, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Eung Yeop Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
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