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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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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 |
Sumario: | 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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