Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure

Alzheimer’s disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positi...

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Autores principales: Gebre, Robel K., Rial, Alexis Moscoso, Raghavan, Sheelakumari, Wiste, Heather J., Johnson Sparrman, Kohl L, Heeman, Fiona, Costoya-Sánchez, Alejandro, Schwarz, Christopher G., Spychalla, Anthony J., Lowe, Val J., Graff-Radford, Jonathan, Knopman, David S., Petersen, Ronald C., Schöll, Michael, Jack, Clifford R., Vemuri, Prashanthi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602128/
https://www.ncbi.nlm.nih.gov/pubmed/37886506
http://dx.doi.org/10.21203/rs.3.rs-3290598/v1
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author Gebre, Robel K.
Rial, Alexis Moscoso
Raghavan, Sheelakumari
Wiste, Heather J.
Johnson Sparrman, Kohl L
Heeman, Fiona
Costoya-Sánchez, Alejandro
Schwarz, Christopher G.
Spychalla, Anthony J.
Lowe, Val J.
Graff-Radford, Jonathan
Knopman, David S.
Petersen, Ronald C.
Schöll, Michael
Jack, Clifford R.
Vemuri, Prashanthi
author_facet Gebre, Robel K.
Rial, Alexis Moscoso
Raghavan, Sheelakumari
Wiste, Heather J.
Johnson Sparrman, Kohl L
Heeman, Fiona
Costoya-Sánchez, Alejandro
Schwarz, Christopher G.
Spychalla, Anthony J.
Lowe, Val J.
Graff-Radford, Jonathan
Knopman, David S.
Petersen, Ronald C.
Schöll, Michael
Jack, Clifford R.
Vemuri, Prashanthi
author_sort Gebre, Robel K.
collection PubMed
description Alzheimer’s disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential.
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spelling pubmed-106021282023-10-27 Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure Gebre, Robel K. Rial, Alexis Moscoso Raghavan, Sheelakumari Wiste, Heather J. Johnson Sparrman, Kohl L Heeman, Fiona Costoya-Sánchez, Alejandro Schwarz, Christopher G. Spychalla, Anthony J. Lowe, Val J. Graff-Radford, Jonathan Knopman, David S. Petersen, Ronald C. Schöll, Michael Jack, Clifford R. Vemuri, Prashanthi Res Sq Article Alzheimer’s disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential. American Journal Experts 2023-10-18 /pmc/articles/PMC10602128/ /pubmed/37886506 http://dx.doi.org/10.21203/rs.3.rs-3290598/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License (https://creativecommons.org/licenses/by/4.0/) https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Gebre, Robel K.
Rial, Alexis Moscoso
Raghavan, Sheelakumari
Wiste, Heather J.
Johnson Sparrman, Kohl L
Heeman, Fiona
Costoya-Sánchez, Alejandro
Schwarz, Christopher G.
Spychalla, Anthony J.
Lowe, Val J.
Graff-Radford, Jonathan
Knopman, David S.
Petersen, Ronald C.
Schöll, Michael
Jack, Clifford R.
Vemuri, Prashanthi
Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure
title Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure
title_full Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure
title_fullStr Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure
title_full_unstemmed Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure
title_short Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure
title_sort advancing tau-pet quantification in alzheimer’s disease with machine learning: introducing theta, a novel tau summary measure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602128/
https://www.ncbi.nlm.nih.gov/pubmed/37886506
http://dx.doi.org/10.21203/rs.3.rs-3290598/v1
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