Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data
Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer's disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and...
Saved in:
| Published in | Alzheimer's research & therapy Vol. 17; no. 1; pp. 134 - 15 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article Web Resource |
| Language | English |
| Published |
England
BioMed Central Ltd
16.06.2025
Springer Science and Business Media LLC BioMed Central BMC |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer's disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials.
The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (Aβ
, P-tau
, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model's effectiveness at patient level was evaluated.
The research cohort's maximum likelihood event sequence comprised CSF Aβ
, P-tau
, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort's order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment.
This study highlights the generalizability of EuroPOND's AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening. |
|---|---|
| AbstractList | Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer's disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials.
The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (Aβ
, P-tau
, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model's effectiveness at patient level was evaluated.
The research cohort's maximum likelihood event sequence comprised CSF Aβ
, P-tau
, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort's order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment.
This study highlights the generalizability of EuroPOND's AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening. Abstract Background Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer’s disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials. Methods The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (Aβ1−42, P-tau181, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model’s effectiveness at patient level was evaluated. Results The research cohort’s maximum likelihood event sequence comprised CSF Aβ1-42, P-tau181, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort’s order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment. Conclusions This study highlights the generalizability of EuroPOND’s AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening. Background Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer's disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials. Methods The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (A[beta].sub.1-42, P-tau.sub.181, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model's effectiveness at patient level was evaluated. Results The research cohort's maximum likelihood event sequence comprised CSF A[beta].sub.1-42, P-tau.sub.181, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort's order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment. Conclusions This study highlights the generalizability of EuroPOND's AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening. Keywords: Alzheimer's disease, Biomarkers, Magnetic resonance imaging, Automated volumetry, Event-based modelling EBM for AD staging is generalizable and reliable across research and real-world clinical datasets. Amnestic MCI subjects exhibited higher EBM scores than non-amnestic subjects, indicating utility for precision recruitment and screening in clinical trials. Tailored EBM models for distinct AD subtypes and diverse neurodegenerative diseases are imperative for accurate staging and understanding varied disease trajectories. Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer's disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials. The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (A[beta].sub.1-42, P-tau.sub.181, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model's effectiveness at patient level was evaluated. The research cohort's maximum likelihood event sequence comprised CSF A[beta].sub.1-42, P-tau.sub.181, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort's order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment. This study highlights the generalizability of EuroPOND's AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening. BACKGROUND: Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer's disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials. METHODS: The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (Aβ1-42, P-tau181, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model's effectiveness at patient level was evaluated. RESULTS: The research cohort's maximum likelihood event sequence comprised CSF Aβ1-42, P-tau181, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort's order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment. CONCLUSIONS: This study highlights the generalizability of EuroPOND's AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening. Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer's disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials.BACKGROUNDEvent-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer's disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials.The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (Aβ1-42, P-tau181, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model's effectiveness at patient level was evaluated.METHODSThe training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (Aβ1-42, P-tau181, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model's effectiveness at patient level was evaluated.The research cohort's maximum likelihood event sequence comprised CSF Aβ1-42, P-tau181, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort's order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment.RESULTSThe research cohort's maximum likelihood event sequence comprised CSF Aβ1-42, P-tau181, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort's order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment.This study highlights the generalizability of EuroPOND's AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening.CONCLUSIONSThis study highlights the generalizability of EuroPOND's AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening. |
| ArticleNumber | 134 |
| Audience | Academic |
| Author | Salmon, Eric Niemantsverdriet, Ellis Engelborghs, Sebastiaan Versijpt, Jan Bier, Jean-Christophe Ribbens, Annemie Ivanoiu, Adrian Benoit, Florence Tournoy, Jos Deryck, Olivier Bjerke, Maria Segers, Kurt Picard, Gaëtane Thiery, Evert Oxtoby, Neil P. Alexander, Daniel C. Struyfs, Hanne de Deyn, Peter Paul Sima, Diana M. Bergmans, Bruno Bellio, Maura van Binst, Anne-Marie Smeets, Dirk Wittens, Mandy M. J. Sieben, Anne Bastin, Christine Brys, Arne De Roeck, Ellen Hanseeuw, Bernard |
| Author_xml | – sequence: 1 givenname: Mandy M. J. surname: Wittens fullname: Wittens, Mandy M. J. – sequence: 2 givenname: Diana M. surname: Sima fullname: Sima, Diana M. – sequence: 3 givenname: Arne surname: Brys fullname: Brys, Arne – sequence: 4 givenname: Hanne surname: Struyfs fullname: Struyfs, Hanne – sequence: 5 givenname: Ellis surname: Niemantsverdriet fullname: Niemantsverdriet, Ellis – sequence: 6 givenname: Ellen surname: De Roeck fullname: De Roeck, Ellen – sequence: 7 givenname: Christine surname: Bastin fullname: Bastin, Christine – sequence: 8 givenname: Florence surname: Benoit fullname: Benoit, Florence – sequence: 9 givenname: Bruno surname: Bergmans fullname: Bergmans, Bruno – sequence: 10 givenname: Jean-Christophe surname: Bier fullname: Bier, Jean-Christophe – sequence: 11 givenname: Peter Paul surname: de Deyn fullname: de Deyn, Peter Paul – sequence: 12 givenname: Olivier surname: Deryck fullname: Deryck, Olivier – sequence: 13 givenname: Bernard surname: Hanseeuw fullname: Hanseeuw, Bernard – sequence: 14 givenname: Adrian surname: Ivanoiu fullname: Ivanoiu, Adrian – sequence: 15 givenname: Gaëtane surname: Picard fullname: Picard, Gaëtane – sequence: 16 givenname: Eric surname: Salmon fullname: Salmon, Eric – sequence: 17 givenname: Kurt surname: Segers fullname: Segers, Kurt – sequence: 18 givenname: Anne surname: Sieben fullname: Sieben, Anne – sequence: 19 givenname: Evert surname: Thiery fullname: Thiery, Evert – sequence: 20 givenname: Jos surname: Tournoy fullname: Tournoy, Jos – sequence: 21 givenname: Anne-Marie surname: van Binst fullname: van Binst, Anne-Marie – sequence: 22 givenname: Jan surname: Versijpt fullname: Versijpt, Jan – sequence: 23 givenname: Dirk surname: Smeets fullname: Smeets, Dirk – sequence: 24 givenname: Maria surname: Bjerke fullname: Bjerke, Maria – sequence: 25 givenname: Maura surname: Bellio fullname: Bellio, Maura – sequence: 26 givenname: Neil P. surname: Oxtoby fullname: Oxtoby, Neil P. – sequence: 27 givenname: Daniel C. surname: Alexander fullname: Alexander, Daniel C. – sequence: 28 givenname: Annemie surname: Ribbens fullname: Ribbens, Annemie – sequence: 29 givenname: Sebastiaan surname: Engelborghs fullname: Engelborghs, Sebastiaan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40524264$$D View this record in MEDLINE/PubMed |
| BookMark | eNptUt2K1DAYLbLi_ugLeCEBQbzp2vw0Ta9k2F11YNn1Qq9DmnztZEmbMWlH1ivBp_D1fBIz03WZASlpDsn5zkm-nNPsaPADZNlLXJxjLPi7iCmuy7wgaeBKiJw_yU5wVYq8xjU92sPH2WmMd0XBORHsWXbMipIwwtlJ9ms5GFhD-g0j2ihnjRqtH5AaDPLT6CyEhJW7jzYi36KrKfjPtzeXaOF-rMD2EP78_B2RsRFUBBRH1dmhQ7034NAUt3hxebPc6QVQLv_ugzNIOztYrRxKdup59rRVLsKLh_ks-_rh6svFp_z69uPyYnGd65KzMSda64rVBS-h4iUmxBjdVtDUtWoqxWlV4KJoakI1r0TVUqpZaUpR4gSpFoaeZctZ13h1J9fB9ircS6-s3C340EkVRqsdSKY0YUwJoCVmoEQNSlOBGzC8aZJX0no_a62npgejU_uCcgeihzuDXcnObyQmuCIUi6RAZ4XU4w6Se2Plhuwqd3hy6ThaNiAJ4UKmO9CySlVvH3yD_zZBHGVvowbn1AB-ipKS9NykJGRr8HqmdirdyA6tTwfRW7pcCMZoQTgmiXX-H1b6DPRWp8C1Nq0fFLzZK1ilNx1X0btpG5t4SHy136LH3vwLXyKQmaCDjzFA-0jBhdwmXM4JlynhcpdwyelfoHvtbA |
| Cites_doi | 10.1111/joim.12816 10.1016/j.neuroimage.2016.03.032 10.1093/brain/awz235 10.1002/alz.12763 10.1002/alz.12906 10.1162/imag_a_00010 10.1186/s13195-016-0176-z 10.1038/s41531-022-00279-x 10.1038/s41583-023-00779-6 10.1093/brain/awu176 10.3233/JAD-171140 10.3389/fneur.2017.00102 10.1586/ern.11.155 10.1016/j.nicl.2019.101954 10.3389/fdata.2021.661110 10.1186/s13195-024-01491-y 10.3233/JAD-170327 10.1212/WNL.0000000000002923 10.1186/s13195-019-0525-9 10.1016/j.neurobiolaging.2019.10.020 10.1002/alz.13859 10.1016/j.neuroimage.2012.01.062 10.1186/s13195-023-01367-7 10.1016/S1474-4422(12)70291-0 10.1093/brain/awz311 10.1016/j.jalz.2018.02.018 10.1016/S1474-4422(20)30314-8 10.1016/j.nicl.2021.102707 10.1038/s41398-024-03084-7 10.1016/j.neuroimage.2018.11.024 10.1016/S1474-4422(18)30403-4 10.1038/ncomms11934 10.1007/s00415-019-09679-1 10.3233/ADR-170013 10.1186/s13195-020-00695-2 10.3390/geriatrics8010012 10.1002/ana.26711 10.1016/j.dadm.2019.01.005 10.1038/s41591-022-01942-9 10.1093/brain/awae203 10.1016/j.jalz.2011.03.003 10.1186/s13195-018-0459-7 10.1001/jamaneurol.2023.5319 10.1016/j.jpsychires.2008.04.014 10.1016/S1474-4422(09)70299-6 10.3233/JAD-210450 |
| ContentType | Journal Article Web Resource |
| Copyright | 2025. The Author(s). COPYRIGHT 2025 BioMed Central Ltd. The Author(s) 2025 2025 |
| Copyright_xml | – notice: 2025. The Author(s). – notice: COPYRIGHT 2025 BioMed Central Ltd. – notice: The Author(s) 2025 2025 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 Q33 5PM DOA |
| DOI | 10.1186/s13195-025-01788-6 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic Université de Liège - Open Repository and Bibliography (ORBI) PubMed Central (Full Participant titles) DOAJ Open Access Full Text |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 1758-9193 |
| EndPage | 15 |
| ExternalDocumentID | oai_doaj_org_article_4ac244a8e3514ea89eac381bed6bba63 PMC12172318 oai_orbi_ulg_ac_be_2268_333357 A844302612 40524264 10_1186_s13195_025_01788_6 |
| Genre | Validation Study Journal Article |
| GeographicLocations | Belgium |
| GeographicLocations_xml | – name: Belgium |
| GrantInformation_xml | – fundername: Interreg V programme Flanders-The Netherlands of the European Regional Development Fund (ERDF) grantid: herinneringen |
| GroupedDBID | --- 0R~ 23M 2WC 53G 5VS 6J9 7X7 88E 8FI 8FJ AAFWJ AAJSJ AASML AAYXX ABDBF ABUWG ACGFS ACIHN ACJQM ACUHS ADBBV ADUKV AEAQA AFKRA AFPKN AHBYD AHMBA AHYZX ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIAM AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C6C CCPQU CITATION DIK E3Z EBD EBLON EBS ESX F5P FYUFA GROUPED_DOAJ GX1 HMCUK HZ~ IAO IEA IHR IHW INH INR ITC KQ8 M1P M~E O5R O5S O9- OK1 P2P P6G PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RBZ ROL RPM RSV SBL SOJ TR2 TUS UKHRP CGR CUY CVF ECM EIF NPM 7X8 Q33 5PM PUEGO |
| ID | FETCH-LOGICAL-c564t-2ccc749065e765122ddcf7eb99ab7a6370100b923c6787f33c45d5851f333c8d3 |
| IEDL.DBID | DOA |
| ISSN | 1758-9193 |
| IngestDate | Wed Aug 27 01:28:34 EDT 2025 Thu Aug 21 18:27:14 EDT 2025 Fri Jul 25 15:16:48 EDT 2025 Fri Jul 11 17:04:04 EDT 2025 Wed Jun 25 16:50:36 EDT 2025 Tue Jun 24 03:41:37 EDT 2025 Tue Jun 24 02:11:03 EDT 2025 Sun Jul 20 01:30:43 EDT 2025 Thu Jul 24 02:15:26 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Biomarkers Automated volumetry Alzheimer’s disease Magnetic resonance imaging Event-based modelling |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c564t-2ccc749065e765122ddcf7eb99ab7a6370100b923c6787f33c45d5851f333c8d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 scopus-id:2-s2.0-105008083351 |
| OpenAccessLink | https://doaj.org/article/4ac244a8e3514ea89eac381bed6bba63 |
| PMID | 40524264 |
| PQID | 3219325228 |
| PQPubID | 23479 |
| PageCount | 15 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_4ac244a8e3514ea89eac381bed6bba63 pubmedcentral_primary_oai_pubmedcentral_nih_gov_12172318 liege_orbi_v2_oai_orbi_ulg_ac_be_2268_333357 proquest_miscellaneous_3219325228 gale_infotracmisc_A844302612 gale_infotracacademiconefile_A844302612 gale_healthsolutions_A844302612 pubmed_primary_40524264 crossref_primary_10_1186_s13195_025_01788_6 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-06-16 |
| PublicationDateYYYYMMDD | 2025-06-16 |
| PublicationDate_xml | – month: 06 year: 2025 text: 2025-06-16 day: 16 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: London |
| PublicationTitle | Alzheimer's research & therapy |
| PublicationTitleAlternate | Alzheimers Res Ther |
| PublicationYear | 2025 |
| Publisher | BioMed Central Ltd Springer Science and Business Media LLC BioMed Central BMC |
| Publisher_xml | – name: BioMed Central Ltd – name: Springer Science and Business Media LLC – name: BioMed Central – name: BMC |
| References | KR Chapman (1788_CR38) 2016; 8 AJ Mitchell (1788_CR37) 2009; 43 CR Jack Jr (1788_CR9) 2010; 9 AL Young (1788_CR24) 2024; 25 CR Jack Jr (1788_CR10) 2013; 12 D Archetti (1788_CR25) 2019; 24 S Karantzoulis (1788_CR17) 2011; 11 HM Fonteijn (1788_CR22) 2012; 60 D Aguillon (1788_CR14) 2023; 19 M Bilgel (1788_CR28) 2019; 11 A Jannati (1788_CR46) 2024; 16 A O’Connor (1788_CR29) 2020; 12 PT Nelson (1788_CR45) 2023; 94 D Bertens (1788_CR4) 2019; 11 CR Jack Jr (1788_CR12) 2016; 87 E Niemantsverdriet (1788_CR32) 2017; 60 N Sheikh-Bahaei (1788_CR8) 2017; 1 RA Huynh (1788_CR6) 2017; 8 G Chetelat (1788_CR7) 2020; 19 M Rakic (1788_CR36) 2021; 31 D Archetti (1788_CR27) 2021; 4 MJ Grothe (1788_CR43) 2023; 19 L Barba (1788_CR44) 2024; 147 E Niemantsverdriet (1788_CR34) 2018; 63 BS Ye (1788_CR20) 2020; 87 E Matar (1788_CR42) 2022; 8 NJ Ashton (1788_CR16) 2024; 81 AL Young (1788_CR26) 2014; 137 V Venkatraghavan (1788_CR21) 2019; 186 PA Wijeratne (1788_CR31) 2023; 1 MMJ Wittens (1788_CR33) 2021; 83 MMJ Wittens (1788_CR35) 2024; 16 CR Jack Jr (1788_CR11) 2024; 20 CR Jack Jr (1788_CR13) 2018; 14 G Wei (1788_CR18) 2020; 267 K Blennow (1788_CR5) 2018; 284 S O’Dowd (1788_CR40) 2019; 142 GBDD Collaborators (1788_CR1) 2019; 18 L Parnetti (1788_CR3) 2019; 11 R Landin-Romero (1788_CR19) 2017; 151 AM Staffaroni (1788_CR30) 2022; 28 Y Iturria-Medina (1788_CR23) 2016; 7 RA Sperling (1788_CR2) 2011; 7 C Della Monica (1788_CR15) 2024; 14 F Salis (1788_CR39) 2023; 8 E Matar (1788_CR41) 2019; 143 |
| References_xml | – volume: 284 start-page: 643 issue: 6 year: 2018 ident: 1788_CR5 publication-title: J Intern Med doi: 10.1111/joim.12816 – volume: 151 start-page: 72 year: 2017 ident: 1788_CR19 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2016.03.032 – volume: 142 start-page: 3338 issue: 11 year: 2019 ident: 1788_CR40 publication-title: Brain doi: 10.1093/brain/awz235 – volume: 19 start-page: 1234 issue: 4 year: 2023 ident: 1788_CR43 publication-title: Alzheimers Dement doi: 10.1002/alz.12763 – volume: 19 start-page: 2585 issue: 6 year: 2023 ident: 1788_CR14 publication-title: Alzheimers Dement doi: 10.1002/alz.12906 – volume: 1 start-page: 1 year: 2023 ident: 1788_CR31 publication-title: Imaging Neurosci (Camb) doi: 10.1162/imag_a_00010 – volume: 8 start-page: 9 issue: 1 year: 2016 ident: 1788_CR38 publication-title: Alzheimers Res Ther doi: 10.1186/s13195-016-0176-z – volume: 8 start-page: 16 issue: 1 year: 2022 ident: 1788_CR42 publication-title: Npj Parkinson’s Disease doi: 10.1038/s41531-022-00279-x – volume: 25 start-page: 111 issue: 2 year: 2024 ident: 1788_CR24 publication-title: Nat Rev Neurosci doi: 10.1038/s41583-023-00779-6 – volume: 137 start-page: 2564 issue: Pt 9 year: 2014 ident: 1788_CR26 publication-title: Brain doi: 10.1093/brain/awu176 – volume: 63 start-page: 1509 issue: 4 year: 2018 ident: 1788_CR34 publication-title: J Alzheimers Dis doi: 10.3233/JAD-171140 – volume: 8 start-page: 102 year: 2017 ident: 1788_CR6 publication-title: Front Neurol doi: 10.3389/fneur.2017.00102 – volume: 11 start-page: 1579 issue: 11 year: 2011 ident: 1788_CR17 publication-title: Expert Rev Neurother doi: 10.1586/ern.11.155 – volume: 24 start-page: 101954 year: 2019 ident: 1788_CR25 publication-title: Neuroimage Clin doi: 10.1016/j.nicl.2019.101954 – volume: 4 start-page: 661110 year: 2021 ident: 1788_CR27 publication-title: Front Big Data doi: 10.3389/fdata.2021.661110 – volume: 16 start-page: 128 issue: 1 year: 2024 ident: 1788_CR35 publication-title: Alzheimers Res Ther doi: 10.1186/s13195-024-01491-y – volume: 60 start-page: 561 issue: 2 year: 2017 ident: 1788_CR32 publication-title: J Alzheimers Dis doi: 10.3233/JAD-170327 – volume: 87 start-page: 539 issue: 5 year: 2016 ident: 1788_CR12 publication-title: Neurology doi: 10.1212/WNL.0000000000002923 – volume: 11 start-page: 74 issue: 1 year: 2019 ident: 1788_CR4 publication-title: Alzheimers Res Ther doi: 10.1186/s13195-019-0525-9 – volume: 87 start-page: 11 year: 2020 ident: 1788_CR20 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2019.10.020 – volume: 20 start-page: 5143 issue: 8 year: 2024 ident: 1788_CR11 publication-title: Alzheimers Dement doi: 10.1002/alz.13859 – volume: 60 start-page: 1880 issue: 3 year: 2012 ident: 1788_CR22 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2012.01.062 – volume: 16 start-page: 2 issue: 1 year: 2024 ident: 1788_CR46 publication-title: Alzheimers Res Ther doi: 10.1186/s13195-023-01367-7 – volume: 12 start-page: 207 issue: 2 year: 2013 ident: 1788_CR10 publication-title: Lancet Neurol doi: 10.1016/S1474-4422(12)70291-0 – volume: 143 start-page: 31 issue: 1 year: 2019 ident: 1788_CR41 publication-title: Brain doi: 10.1093/brain/awz311 – volume: 14 start-page: 535 issue: 4 year: 2018 ident: 1788_CR13 publication-title: Alzheimers Dement doi: 10.1016/j.jalz.2018.02.018 – volume: 19 start-page: 951 issue: 11 year: 2020 ident: 1788_CR7 publication-title: Lancet Neurol doi: 10.1016/S1474-4422(20)30314-8 – volume: 31 start-page: 102707 year: 2021 ident: 1788_CR36 publication-title: Neuroimage Clin doi: 10.1016/j.nicl.2021.102707 – volume: 14 start-page: 373 issue: 1 year: 2024 ident: 1788_CR15 publication-title: Transl Psychiatry doi: 10.1038/s41398-024-03084-7 – volume: 186 start-page: 518 year: 2019 ident: 1788_CR21 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2018.11.024 – volume: 18 start-page: 88 issue: 1 year: 2019 ident: 1788_CR1 publication-title: Lancet Neurol doi: 10.1016/S1474-4422(18)30403-4 – volume: 7 start-page: 11934 year: 2016 ident: 1788_CR23 publication-title: Nat Commun doi: 10.1038/ncomms11934 – volume: 267 start-page: 1086 issue: 4 year: 2020 ident: 1788_CR18 publication-title: J Neurol doi: 10.1007/s00415-019-09679-1 – volume: 1 start-page: 71 issue: 1 year: 2017 ident: 1788_CR8 publication-title: J Alzheimers Dis Rep doi: 10.3233/ADR-170013 – volume: 12 start-page: 126 issue: 1 year: 2020 ident: 1788_CR29 publication-title: Alzheimers Res Ther doi: 10.1186/s13195-020-00695-2 – volume: 8 start-page: 12 issue: 1 year: 2023 ident: 1788_CR39 publication-title: Geriatrics doi: 10.3390/geriatrics8010012 – volume: 94 start-page: 211 issue: 2 year: 2023 ident: 1788_CR45 publication-title: Ann Neurol doi: 10.1002/ana.26711 – volume: 11 start-page: 205 year: 2019 ident: 1788_CR28 publication-title: Alzheimers Dement (Amst) doi: 10.1016/j.dadm.2019.01.005 – volume: 28 start-page: 2194 issue: 10 year: 2022 ident: 1788_CR30 publication-title: Nat Med doi: 10.1038/s41591-022-01942-9 – volume: 147 start-page: 3325 issue: 10 year: 2024 ident: 1788_CR44 publication-title: Brain doi: 10.1093/brain/awae203 – volume: 7 start-page: 280 issue: 3 year: 2011 ident: 1788_CR2 publication-title: Alzheimers Dement doi: 10.1016/j.jalz.2011.03.003 – volume: 11 start-page: 7 issue: 1 year: 2019 ident: 1788_CR3 publication-title: Alzheimers Res Ther doi: 10.1186/s13195-018-0459-7 – volume: 81 start-page: 255 issue: 3 year: 2024 ident: 1788_CR16 publication-title: JAMA Neurol doi: 10.1001/jamaneurol.2023.5319 – volume: 43 start-page: 411 issue: 4 year: 2009 ident: 1788_CR37 publication-title: J Psychiatr Res doi: 10.1016/j.jpsychires.2008.04.014 – volume: 9 start-page: 119 issue: 1 year: 2010 ident: 1788_CR9 publication-title: Lancet Neurol doi: 10.1016/S1474-4422(09)70299-6 – volume: 83 start-page: 623 issue: 2 year: 2021 ident: 1788_CR33 publication-title: J Alzheimers Dis doi: 10.3233/JAD-210450 |
| RestrictionsOnAccess | open access |
| SSID | ssj0066284 |
| Score | 2.3664525 |
| Snippet | Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This... Background Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling... BACKGROUND: Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling... EBM for AD staging is generalizable and reliable across research and real-world clinical datasets. Amnestic MCI subjects exhibited higher EBM scores than... Abstract Background Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling... |
| SourceID | doaj pubmedcentral liege proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | 134 |
| SubjectTerms | Advertising executives Aged Aged, 80 and over Alzheimer Disease - cerebrospinal fluid Alzheimer Disease - diagnosis Alzheimer Disease - diagnostic imaging Alzheimer Disease - pathology Alzheimer's disease Amyloid beta-Peptides Amyloid beta-Peptides - cerebrospinal fluid Automated volumetry Belgium Biomarkers Biomarkers - cerebrospinal fluid Cognitive Dysfunction - cerebrospinal fluid Cohort Studies Diagnosis Disease Progression Diseases Evaluation Event-based modelling Female Humans Machine learning Magnetic Resonance Imaging Male Medical research Medicine, Experimental Mental Status and Dementia Tests Middle Aged Neuroimaging Neuropsychological Tests Neurosciences & behavior Neurosciences & comportement Physiological aspects Sciences sociales & comportementales, psychologie Social & behavioral sciences, psychology tau Proteins tau Proteins - cerebrospinal fluid |
| Title | Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40524264 https://www.proquest.com/docview/3219325228 http://orbi.ulg.ac.be/handle/2268/333357 https://pubmed.ncbi.nlm.nih.gov/PMC12172318 https://doaj.org/article/4ac244a8e3514ea89eac381bed6bba63 |
| Volume | 17 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELagvXBBRbwCZTESEgeImsSJ7Ry321YtEkuFqLQ3y6-0Ky1Z1N3tgRMSv4K_xy9hxs5GDULiws2bOJF3Hp5v4nkQ8tpaLo3PWApQHhwUV-agUk0GI5PxhhtdcExO_jDlpxfl-1k1u9XqC2PCYnngSLiDUluwQFp6DDn3WtawU4CVMd5xYzQPdT7B5m2dqbgHcw677jZFRvKDVc7ykImMgWrg9KV8YIZCtf5-T95d4HH13xDnn4GTtyzRyR6530FIOo5Lf0Du-PYh-XHW97NdU5CeeeyVRHXrKAb9gPWDcSxAQpcNxa_w5x-nR3S8-Hbl51_89a_vP1e0O6-hgBmxexENjXIoBsdf0vHR9Cy8D3DmIg2lVuk2sZJipOkjcnFy_HlymnYNFlJb8XKdFtZaUdaAQrzgYPkL52wjvKlrbQTQVoCzlhmAgBZMmmgYs2Xl8BwRhsxKxx6TnXbZ-qeEMmNyYWvuqkqU8HSdNRUDbyrT1hVO24S83dJbfY11NFTwPyRXkTsKuKMCdxRPyCGypJ-JNbDDBZAM1UmG-pdkJOQlMlTFhNJek9VYliULpdMS8ibMQF0GflrdpSTAX8KqWIOZ-4OZoIN2cPtdEBpYn5mrmyIsOIw3C1iwVcYrwLlSAd1YJRLyaitbCt-EcW6tX25WihUIqAETy4Q8ibLWEwGgdcCvCZEDKRxQaXinnV-FiuE5tiGD3fvZ_6Drc3KvCJrE05zvk5319ca_AGS2NiNyV8zEiOweHk_PP8GvCZ-MgmL-Bp11O2I |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Independent+validation+and+outlier+analysis+of+EuroPOND+alzheimer%27s+disease+staging+model+using+ADNI+and+real-world+clinical+data&rft.jtitle=Alzheimer%27s+research+%26+therapy&rft.au=Bastin%2C+Christine&rft.au=Struyfs%2C+Hanne&rft.au=Brys%2C+Arne&rft.au=van+Binst%2C+Anne-Marie&rft.date=2025-06-16&rft.pub=BioMed+Central+Ltd&rft.issn=1758-9193&rft.eissn=1758-9193&rft.volume=17&rft.issue=1&rft_id=info:doi/10.1186%2Fs13195-025-01788-6&rft.externalDBID=n%2Fa&rft.externalDocID=A844302612 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1758-9193&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1758-9193&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1758-9193&client=summon |