Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example

Background Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jin, Sameer Omer [verfasserIn] Mérida, Inés Stavropoulos, Ioannis Elwes, Robert D. C. Lam, Tanya Guedj, Eric Girard, Nadine Costes, Nicolas Hammers, Alexander

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Statistical parametric mapping (SPM) Harmonisation Anomaly detection Interscanner differences Smoothness

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: EJNMMI Research - Berlin : Springer, 2011, 13(2023), 1 vom: 15. Nov.
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:1 ; day:15 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s13550-023-01023-z

Katalog-ID:	SPR053742168

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allfields	10.1186/s13550-023-01023-z doi (DE-627)SPR053742168 (SPR)s13550-023-01023-z-e DE-627 ger DE-627 rakwb eng Jin, Sameer Omer verfasserin (orcid)0000-0002-1643-2735 aut Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation. Statistical parametric mapping (SPM) (dpeaa)DE-He213 Harmonisation (dpeaa)DE-He213 Anomaly detection (dpeaa)DE-He213 Interscanner differences (dpeaa)DE-He213 Smoothness (dpeaa)DE-He213 Mérida, Inés (orcid)0000-0003-1436-4684 aut Stavropoulos, Ioannis (orcid)0000-0002-6920-1826 aut Elwes, Robert D. C. aut Lam, Tanya (orcid)0000-0001-6986-7727 aut Guedj, Eric (orcid)0000-0003-1912-6132 aut Girard, Nadine (orcid)0000-0001-8639-9275 aut Costes, Nicolas (orcid)0000-0002-2954-9262 aut Hammers, Alexander (orcid)0000-0001-9530-4848 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 15. Nov. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:15 month:11 https://dx.doi.org/10.1186/s13550-023-01023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 15 11
spelling	10.1186/s13550-023-01023-z doi (DE-627)SPR053742168 (SPR)s13550-023-01023-z-e DE-627 ger DE-627 rakwb eng Jin, Sameer Omer verfasserin (orcid)0000-0002-1643-2735 aut Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation. Statistical parametric mapping (SPM) (dpeaa)DE-He213 Harmonisation (dpeaa)DE-He213 Anomaly detection (dpeaa)DE-He213 Interscanner differences (dpeaa)DE-He213 Smoothness (dpeaa)DE-He213 Mérida, Inés (orcid)0000-0003-1436-4684 aut Stavropoulos, Ioannis (orcid)0000-0002-6920-1826 aut Elwes, Robert D. C. aut Lam, Tanya (orcid)0000-0001-6986-7727 aut Guedj, Eric (orcid)0000-0003-1912-6132 aut Girard, Nadine (orcid)0000-0001-8639-9275 aut Costes, Nicolas (orcid)0000-0002-2954-9262 aut Hammers, Alexander (orcid)0000-0001-9530-4848 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 15. Nov. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:15 month:11 https://dx.doi.org/10.1186/s13550-023-01023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 15 11
allfields_unstemmed	10.1186/s13550-023-01023-z doi (DE-627)SPR053742168 (SPR)s13550-023-01023-z-e DE-627 ger DE-627 rakwb eng Jin, Sameer Omer verfasserin (orcid)0000-0002-1643-2735 aut Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation. Statistical parametric mapping (SPM) (dpeaa)DE-He213 Harmonisation (dpeaa)DE-He213 Anomaly detection (dpeaa)DE-He213 Interscanner differences (dpeaa)DE-He213 Smoothness (dpeaa)DE-He213 Mérida, Inés (orcid)0000-0003-1436-4684 aut Stavropoulos, Ioannis (orcid)0000-0002-6920-1826 aut Elwes, Robert D. C. aut Lam, Tanya (orcid)0000-0001-6986-7727 aut Guedj, Eric (orcid)0000-0003-1912-6132 aut Girard, Nadine (orcid)0000-0001-8639-9275 aut Costes, Nicolas (orcid)0000-0002-2954-9262 aut Hammers, Alexander (orcid)0000-0001-9530-4848 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 15. Nov. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:15 month:11 https://dx.doi.org/10.1186/s13550-023-01023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 15 11
allfieldsGer	10.1186/s13550-023-01023-z doi (DE-627)SPR053742168 (SPR)s13550-023-01023-z-e DE-627 ger DE-627 rakwb eng Jin, Sameer Omer verfasserin (orcid)0000-0002-1643-2735 aut Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation. Statistical parametric mapping (SPM) (dpeaa)DE-He213 Harmonisation (dpeaa)DE-He213 Anomaly detection (dpeaa)DE-He213 Interscanner differences (dpeaa)DE-He213 Smoothness (dpeaa)DE-He213 Mérida, Inés (orcid)0000-0003-1436-4684 aut Stavropoulos, Ioannis (orcid)0000-0002-6920-1826 aut Elwes, Robert D. C. aut Lam, Tanya (orcid)0000-0001-6986-7727 aut Guedj, Eric (orcid)0000-0003-1912-6132 aut Girard, Nadine (orcid)0000-0001-8639-9275 aut Costes, Nicolas (orcid)0000-0002-2954-9262 aut Hammers, Alexander (orcid)0000-0001-9530-4848 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 15. Nov. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:15 month:11 https://dx.doi.org/10.1186/s13550-023-01023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 15 11
allfieldsSound	10.1186/s13550-023-01023-z doi (DE-627)SPR053742168 (SPR)s13550-023-01023-z-e DE-627 ger DE-627 rakwb eng Jin, Sameer Omer verfasserin (orcid)0000-0002-1643-2735 aut Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation. Statistical parametric mapping (SPM) (dpeaa)DE-He213 Harmonisation (dpeaa)DE-He213 Anomaly detection (dpeaa)DE-He213 Interscanner differences (dpeaa)DE-He213 Smoothness (dpeaa)DE-He213 Mérida, Inés (orcid)0000-0003-1436-4684 aut Stavropoulos, Ioannis (orcid)0000-0002-6920-1826 aut Elwes, Robert D. C. aut Lam, Tanya (orcid)0000-0001-6986-7727 aut Guedj, Eric (orcid)0000-0003-1912-6132 aut Girard, Nadine (orcid)0000-0001-8639-9275 aut Costes, Nicolas (orcid)0000-0002-2954-9262 aut Hammers, Alexander (orcid)0000-0001-9530-4848 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 15. Nov. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:15 month:11 https://dx.doi.org/10.1186/s13550-023-01023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 15 11
language	English
source	Enthalten in EJNMMI Research 13(2023), 1 vom: 15. Nov. volume:13 year:2023 number:1 day:15 month:11
sourceStr	Enthalten in EJNMMI Research 13(2023), 1 vom: 15. Nov. volume:13 year:2023 number:1 day:15 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Statistical parametric mapping (SPM) Harmonisation Anomaly detection Interscanner differences Smoothness
isfreeaccess_bool	true
container_title	EJNMMI Research
authorswithroles_txt_mv	Jin, Sameer Omer @@aut@@ Mérida, Inés @@aut@@ Stavropoulos, Ioannis @@aut@@ Elwes, Robert D. C. @@aut@@ Lam, Tanya @@aut@@ Guedj, Eric @@aut@@ Girard, Nadine @@aut@@ Costes, Nicolas @@aut@@ Hammers, Alexander @@aut@@
publishDateDaySort_date	2023-11-15T00:00:00Z
hierarchy_top_id	664970265
id	SPR053742168
language_de	englisch
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Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. 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author	Jin, Sameer Omer
spellingShingle	Jin, Sameer Omer misc Statistical parametric mapping (SPM) misc Harmonisation misc Anomaly detection misc Interscanner differences misc Smoothness Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example
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topic_title	Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example Statistical parametric mapping (SPM) (dpeaa)DE-He213 Harmonisation (dpeaa)DE-He213 Anomaly detection (dpeaa)DE-He213 Interscanner differences (dpeaa)DE-He213 Smoothness (dpeaa)DE-He213
topic	misc Statistical parametric mapping (SPM) misc Harmonisation misc Anomaly detection misc Interscanner differences misc Smoothness
topic_unstemmed	misc Statistical parametric mapping (SPM) misc Harmonisation misc Anomaly detection misc Interscanner differences misc Smoothness
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title	Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example
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title_full	Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example
author_sort	Jin, Sameer Omer
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author_browse	Jin, Sameer Omer Mérida, Inés Stavropoulos, Ioannis Elwes, Robert D. C. Lam, Tanya Guedj, Eric Girard, Nadine Costes, Nicolas Hammers, Alexander
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author-letter	Jin, Sameer Omer
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title_sort	characterisation of a novel [18f]fdg brain pet database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example
title_auth	Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example
abstract	Background Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation. © The Author(s) 2023
abstractGer	Background Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation. © The Author(s) 2023
abstract_unstemmed	Background Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). Results Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). Conclusions As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation. © The Author(s) 2023
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title_short	Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example
url	https://dx.doi.org/10.1186/s13550-023-01023-z
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author2	Mérida, Inés Stavropoulos, Ioannis Elwes, Robert D. C. Lam, Tanya Guedj, Eric Girard, Nadine Costes, Nicolas Hammers, Alexander
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