Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT
Abstract 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 [123I]FP-CIT in different settings. Methods The first subject sample comprised 645 subjects from th...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-05-01
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| Series: | EJNMMI Physics |
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| Online Access: | http://link.springer.com/article/10.1186/s40658-020-00304-z |
| _version_ | 1818133649077305344 |
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| author | Helen Schmitz-Steinkrüger Catharina Lange Ivayla Apostolova Holger Amthauer Wencke Lehnert Susanne Klutmann Ralph Buchert |
| author_facet | Helen Schmitz-Steinkrüger Catharina Lange Ivayla Apostolova Holger Amthauer Wencke Lehnert Susanne Klutmann Ralph Buchert |
| author_sort | Helen Schmitz-Steinkrüger |
| collection | DOAJ |
| description | Abstract 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 [123I]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. |
| first_indexed | 2024-12-11T08:56:04Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2197-7364 |
| language | English |
| last_indexed | 2024-12-11T08:56:04Z |
| publishDate | 2020-05-01 |
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| spelling | doaj.art-71aa7745a0aa4c2ebd3bec592bea09502022-12-22T01:13:52ZengSpringerOpenEJNMMI Physics2197-73642020-05-017111610.1186/s40658-020-00304-zImpact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECTHelen Schmitz-Steinkrüger0Catharina Lange1Ivayla Apostolova2Holger Amthauer3Wencke Lehnert4Susanne Klutmann5Ralph Buchert6Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-EppendorfDepartment of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthDepartment for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-EppendorfDepartment of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthDepartment for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-EppendorfDepartment for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-EppendorfDepartment for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-EppendorfAbstract 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 [123I]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.http://link.springer.com/article/10.1186/s40658-020-00304-zDopamine transporterSPECTFP-CITSpecific binding ratioNormal database |
| spellingShingle | Helen Schmitz-Steinkrüger Catharina Lange Ivayla Apostolova Holger Amthauer Wencke Lehnert Susanne Klutmann Ralph Buchert Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT EJNMMI Physics Dopamine transporter SPECT FP-CIT Specific binding ratio Normal database |
| 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 | Dopamine transporter SPECT FP-CIT Specific binding ratio Normal database |
| url | http://link.springer.com/article/10.1186/s40658-020-00304-z |
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