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Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT

BACKGROUND: This study investigated the impact of the size of the normal database on the classification performance of the specific binding ratio (SBR) in dopamine transporter (DAT) SPECT with [(123)I]FP-CIT in different settings. METHODS: The first subject sample comprised 645 subjects from the Par...

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Autores principales: Schmitz-Steinkrüger, Helen, Lange, Catharina, Apostolova, Ivayla, Amthauer, Holger, Lehnert, Wencke, Klutmann, Susanne, Buchert, Ralph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239986/
https://www.ncbi.nlm.nih.gov/pubmed/32435936
http://dx.doi.org/10.1186/s40658-020-00304-z
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author Schmitz-Steinkrüger, Helen
Lange, Catharina
Apostolova, Ivayla
Amthauer, Holger
Lehnert, Wencke
Klutmann, Susanne
Buchert, Ralph
author_facet Schmitz-Steinkrüger, Helen
Lange, Catharina
Apostolova, Ivayla
Amthauer, Holger
Lehnert, Wencke
Klutmann, Susanne
Buchert, Ralph
author_sort Schmitz-Steinkrüger, Helen
collection PubMed
description BACKGROUND: This study investigated the impact of the size of the normal database on the classification performance of the specific binding ratio (SBR) in dopamine transporter (DAT) SPECT with [(123)I]FP-CIT in different settings. METHODS: The first subject sample comprised 645 subjects from the Parkinson’s Progression Marker Initiative (PPMI), 207 healthy controls (HC), and 438 Parkinson’s disease (PD) patients. The second sample comprised 372 patients from clinical routine patient care, 186 with non-neurodegenerative parkinsonian syndrome (PS) and 186 with neurodegenerative PS. Single-photon emission computed tomography (SPECT) images of the clinical sample were reconstructed with two different reconstruction algorithms (filtered backprojection, iterative ordered subsets expectation maximization (OSEM) reconstruction with resolution recovery). The putaminal specific binding ratio (SBR) was computed using an anatomical region of interest (ROI) predefined in standard (MNI) space in the Automated Anatomic Labeling (AAL) atlas or using hottest voxels (HV) analysis in large predefined ROIs. SBR values were transformed to z-scores using mean and standard deviation of the SBR in a normal database of varying sizes (n = 5, 10, 15,…, 50) randomly selected from the HC subjects (PPMI sample) or the patients with non-neurodegenerative PS (clinical sample). Accuracy, sensitivity, and specificity for identifying patients with PD or neurodegenerative PS were determined as performance measures using a predefined fixed cutoff on the z-score. This was repeated for 10,000 randomly selected normal databases, separately for each size of the normal database. Mean and 5th percentile of the performance measures over the 10,000 realizations were computed. Accuracy, sensitivity, and specificity when using the whole set of HC or non-neurodegenerative PS subjects as normal database were used as benchmark. RESULTS: Mean loss of accuracy of the putamen SBR z-score was below 1% when the normal database included at least 15 subjects, independent of subject sample (PPMI or clinical), reconstruction method (filtered backprojection or OSEM), and ROI method (AAL or HV). However, the variability of the accuracy of the putamen SBR z-score decreased monotonically with increasing size of normal database and was still considerable at size 15. In order to achieve less than 5% “maximum” loss of accuracy (defined by the 5th percentile) in all settings required at least 25 to 30 subjects in the normal database. Reduction of mean and “maximum” loss of accuracy of the putamen SBR z-score by further increasing the size of the normal database was very small beyond size 40. CONCLUSIONS: The results of this study suggest that 25 to 30 is the minimum size of the normal database to reliably achieve good performance of semi-quantitative analysis in dopamine transporter (DAT) SPECT, independent of the algorithm used for image reconstruction and the ROI method used to estimate the putaminal SBR.
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spelling pubmed-72399862020-05-27 Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT Schmitz-Steinkrüger, Helen Lange, Catharina Apostolova, Ivayla Amthauer, Holger Lehnert, Wencke Klutmann, Susanne Buchert, Ralph EJNMMI Phys Original Research BACKGROUND: This study investigated the impact of the size of the normal database on the classification performance of the specific binding ratio (SBR) in dopamine transporter (DAT) SPECT with [(123)I]FP-CIT in different settings. METHODS: The first subject sample comprised 645 subjects from the Parkinson’s Progression Marker Initiative (PPMI), 207 healthy controls (HC), and 438 Parkinson’s disease (PD) patients. The second sample comprised 372 patients from clinical routine patient care, 186 with non-neurodegenerative parkinsonian syndrome (PS) and 186 with neurodegenerative PS. Single-photon emission computed tomography (SPECT) images of the clinical sample were reconstructed with two different reconstruction algorithms (filtered backprojection, iterative ordered subsets expectation maximization (OSEM) reconstruction with resolution recovery). The putaminal specific binding ratio (SBR) was computed using an anatomical region of interest (ROI) predefined in standard (MNI) space in the Automated Anatomic Labeling (AAL) atlas or using hottest voxels (HV) analysis in large predefined ROIs. SBR values were transformed to z-scores using mean and standard deviation of the SBR in a normal database of varying sizes (n = 5, 10, 15,…, 50) randomly selected from the HC subjects (PPMI sample) or the patients with non-neurodegenerative PS (clinical sample). Accuracy, sensitivity, and specificity for identifying patients with PD or neurodegenerative PS were determined as performance measures using a predefined fixed cutoff on the z-score. This was repeated for 10,000 randomly selected normal databases, separately for each size of the normal database. Mean and 5th percentile of the performance measures over the 10,000 realizations were computed. Accuracy, sensitivity, and specificity when using the whole set of HC or non-neurodegenerative PS subjects as normal database were used as benchmark. RESULTS: Mean loss of accuracy of the putamen SBR z-score was below 1% when the normal database included at least 15 subjects, independent of subject sample (PPMI or clinical), reconstruction method (filtered backprojection or OSEM), and ROI method (AAL or HV). However, the variability of the accuracy of the putamen SBR z-score decreased monotonically with increasing size of normal database and was still considerable at size 15. In order to achieve less than 5% “maximum” loss of accuracy (defined by the 5th percentile) in all settings required at least 25 to 30 subjects in the normal database. Reduction of mean and “maximum” loss of accuracy of the putamen SBR z-score by further increasing the size of the normal database was very small beyond size 40. CONCLUSIONS: The results of this study suggest that 25 to 30 is the minimum size of the normal database to reliably achieve good performance of semi-quantitative analysis in dopamine transporter (DAT) SPECT, independent of the algorithm used for image reconstruction and the ROI method used to estimate the putaminal SBR. Springer International Publishing 2020-05-20 /pmc/articles/PMC7239986/ /pubmed/32435936 http://dx.doi.org/10.1186/s40658-020-00304-z Text en © The Author(s) 2020 Open AccessThis 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/.
spellingShingle Original Research
Schmitz-Steinkrüger, Helen
Lange, Catharina
Apostolova, Ivayla
Amthauer, Holger
Lehnert, Wencke
Klutmann, Susanne
Buchert, Ralph
Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT
title Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT
title_full Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT
title_fullStr Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT
title_full_unstemmed Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT
title_short Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT
title_sort impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter spect
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239986/
https://www.ncbi.nlm.nih.gov/pubmed/32435936
http://dx.doi.org/10.1186/s40658-020-00304-z
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