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Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake

PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using (123)I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane ((123)I-FP-CIT) single-photon emission computerized tomography (SPECT) can evaluate Parkinsonism. Nigral hyperintensity...

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Autores principales: Bae, Yun Jung, Choi, Byung Se, Kim, Jong-Min, AI, Walid Abdullah, Yun, Ildong, Song, Yoo Sung, Nam, Yoonho, Cho, Se Jin, Kim, Jae Hyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10271910/
https://www.ncbi.nlm.nih.gov/pubmed/37209181
http://dx.doi.org/10.1007/s00234-023-03168-z
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author Bae, Yun Jung
Choi, Byung Se
Kim, Jong-Min
AI, Walid Abdullah
Yun, Ildong
Song, Yoo Sung
Nam, Yoonho
Cho, Se Jin
Kim, Jae Hyoung
author_facet Bae, Yun Jung
Choi, Byung Se
Kim, Jong-Min
AI, Walid Abdullah
Yun, Ildong
Song, Yoo Sung
Nam, Yoonho
Cho, Se Jin
Kim, Jae Hyoung
author_sort Bae, Yun Jung
collection PubMed
description PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using (123)I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane ((123)I-FP-CIT) single-photon emission computerized tomography (SPECT) can evaluate Parkinsonism. Nigral hyperintensity from nigrosome-1 and striatal dopamine transporter uptake are reduced in Parkinsonism; however, quantification is only possible with SPECT. Here, we aimed to develop a deep-learning-based regressor model that can predict striatal (123)I-FP-CIT uptake on nigrosome magnetic resonance imaging (MRI) as a biomarker for Parkinsonism. METHODS: Between February 2017 and December 2018, participants who underwent 3 T brain MRI including SWI and (123)I-FP-CIT SPECT based on suspected Parkinsonism were included. Two neuroradiologists evaluated the nigral hyperintensity and annotated the centroids of nigrosome-1 structures. We used a convolutional neural network-based regression model to predict striatal specific binding ratios (SBRs) measured via SPECT using the cropped nigrosome images. The correlation between measured and predicted SBRs was evaluated. RESULTS: We included 367 participants (203 women (55.3%); age, 69.0 ± 9.2 [range, 39–88] years). Random data from 293 participants (80%) were used for training. In the test set (74 participants [20%]), the measured and predicted (123)I-FP-CIT SBRs were significantly lower with the loss of nigral hyperintensity (2.31 ± 0.85 vs. 2.44 ± 0.90) than with intact nigral hyperintensity (4.16 ± 1.24 vs. 4.21 ± 1.35, P < 0.01). The sorted measured (123)I-FP-CIT SBRs and the corresponding predicted values were significantly and positively correlated (ρ(c) = 0.7443; 95% confidence interval, 0.6216–0.8314; P < 0.01). CONCLUSION: A deep learning-based regressor model effectively predicted striatal (123)I-FP-CIT SBRs based on nigrosome MRI with high correlation using manually-measured values, enabling nigrosome MRI as a biomarker for nigrostriatal dopaminergic degeneration in Parkinsonism. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00234-023-03168-z.
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spelling pubmed-102719102023-06-17 Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake Bae, Yun Jung Choi, Byung Se Kim, Jong-Min AI, Walid Abdullah Yun, Ildong Song, Yoo Sung Nam, Yoonho Cho, Se Jin Kim, Jae Hyoung Neuroradiology Diagnostic Neuroradiology PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using (123)I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane ((123)I-FP-CIT) single-photon emission computerized tomography (SPECT) can evaluate Parkinsonism. Nigral hyperintensity from nigrosome-1 and striatal dopamine transporter uptake are reduced in Parkinsonism; however, quantification is only possible with SPECT. Here, we aimed to develop a deep-learning-based regressor model that can predict striatal (123)I-FP-CIT uptake on nigrosome magnetic resonance imaging (MRI) as a biomarker for Parkinsonism. METHODS: Between February 2017 and December 2018, participants who underwent 3 T brain MRI including SWI and (123)I-FP-CIT SPECT based on suspected Parkinsonism were included. Two neuroradiologists evaluated the nigral hyperintensity and annotated the centroids of nigrosome-1 structures. We used a convolutional neural network-based regression model to predict striatal specific binding ratios (SBRs) measured via SPECT using the cropped nigrosome images. The correlation between measured and predicted SBRs was evaluated. RESULTS: We included 367 participants (203 women (55.3%); age, 69.0 ± 9.2 [range, 39–88] years). Random data from 293 participants (80%) were used for training. In the test set (74 participants [20%]), the measured and predicted (123)I-FP-CIT SBRs were significantly lower with the loss of nigral hyperintensity (2.31 ± 0.85 vs. 2.44 ± 0.90) than with intact nigral hyperintensity (4.16 ± 1.24 vs. 4.21 ± 1.35, P < 0.01). The sorted measured (123)I-FP-CIT SBRs and the corresponding predicted values were significantly and positively correlated (ρ(c) = 0.7443; 95% confidence interval, 0.6216–0.8314; P < 0.01). CONCLUSION: A deep learning-based regressor model effectively predicted striatal (123)I-FP-CIT SBRs based on nigrosome MRI with high correlation using manually-measured values, enabling nigrosome MRI as a biomarker for nigrostriatal dopaminergic degeneration in Parkinsonism. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00234-023-03168-z. Springer Berlin Heidelberg 2023-05-20 2023 /pmc/articles/PMC10271910/ /pubmed/37209181 http://dx.doi.org/10.1007/s00234-023-03168-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Diagnostic Neuroradiology
Bae, Yun Jung
Choi, Byung Se
Kim, Jong-Min
AI, Walid Abdullah
Yun, Ildong
Song, Yoo Sung
Nam, Yoonho
Cho, Se Jin
Kim, Jae Hyoung
Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake
title Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake
title_full Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake
title_fullStr Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake
title_full_unstemmed Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake
title_short Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake
title_sort deep learning regressor model based on nigrosome mri in parkinson syndrome effectively predicts striatal dopamine transporter-spect uptake
topic Diagnostic Neuroradiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10271910/
https://www.ncbi.nlm.nih.gov/pubmed/37209181
http://dx.doi.org/10.1007/s00234-023-03168-z
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