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Optimal Target Region for Subject Classification on the Basis of Amyloid PET Images.
J Nucl Med 2015; 56(9):1351-8JN

Abstract

Classification of subjects on the basis of amyloid PET scans is increasingly being used in research studies and clinical practice. Although qualitative, visual assessment is currently the gold standard approach, automated classification techniques are inherently more reproducible and efficient. The objective of this work was to develop a statistical approach for the automated classification of subjects with different levels of cognitive impairment into a group with low amyloid levels (AβL) and a group with high amyloid levels (AβH) through the use of amyloid PET data from the Alzheimer Disease Neuroimaging Initiative study.

METHODS

In our framework, an iterative, voxelwise, regularized discriminant analysis is combined with a receiver operating characteristic approach that optimizes the selection of a region of interest (ROI) and a cutoff value for the automated classification of subjects into the AβL and AβH groups. The robustness, spatial stability, and generalization of the resulting target ROIs were evaluated by use of the standardized uptake value ratio for (18)F-florbetapir PET images from subjects who served as healthy controls, subjects who had mild cognitive impairment, and subjects who had Alzheimer disease and were participating in the Alzheimer Disease Neuroimaging Initiative study.

RESULTS

We determined that several iterations of the discriminant analysis improved the classification of subjects into the AβL and AβH groups. We found that an ROI consisting of the posterior cingulate cortex/precuneus and the medial frontal cortex yielded optimal group separation and showed good stability across different reference regions and cognitive cohorts. A key step in this process was the automated determination of the cutoff value for group separation, which was dependent on the reference region used for the standardized uptake value ratio calculation and which was shown to have a relatively narrow range across subject groups.

CONCLUSION

We developed a data-driven approach for the determination of an optimal target ROI and an associated cutoff value for the separation of subjects into the AβL and AβH groups. Future work should include the application of this process to other datasets to facilitate the determination of the translatability of the optimal ROI obtained in this study to other populations. Ideally, the accuracy of our target ROI and cutoff value could be further validated with PET-autopsy data from large-scale studies. It is anticipated that this approach will be extremely useful for the enrichment of study populations in clinical trials involving putative disease-modifying therapeutic agents for Alzheimer disease.

Authors+Show Affiliations

Biospective Inc., Montreal, Quebec, Canada; and.Biospective Inc., Montreal, Quebec, Canada; and.Biospective Inc., Montreal, Quebec, Canada; and.Biospective Inc., Montreal, Quebec, Canada; and.No affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

26135108

Citation

Carbonell, Felix, et al. "Optimal Target Region for Subject Classification On the Basis of Amyloid PET Images." Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine, vol. 56, no. 9, 2015, pp. 1351-8.
Carbonell F, Zijdenbos AP, Charil A, et al. Optimal Target Region for Subject Classification on the Basis of Amyloid PET Images. J Nucl Med. 2015;56(9):1351-8.
Carbonell, F., Zijdenbos, A. P., Charil, A., Grand'Maison, M., & Bedell, B. J. (2015). Optimal Target Region for Subject Classification on the Basis of Amyloid PET Images. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine, 56(9), pp. 1351-8. doi:10.2967/jnumed.115.158774.
Carbonell F, et al. Optimal Target Region for Subject Classification On the Basis of Amyloid PET Images. J Nucl Med. 2015;56(9):1351-8. PubMed PMID: 26135108.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Optimal Target Region for Subject Classification on the Basis of Amyloid PET Images. AU - Carbonell,Felix, AU - Zijdenbos,Alex P, AU - Charil,Arnaud, AU - Grand'Maison,Marilyn, AU - Bedell,Barry J, AU - ,, Y1 - 2015/07/01/ PY - 2015/04/15/received PY - 2015/06/16/accepted PY - 2015/7/3/entrez PY - 2015/7/3/pubmed PY - 2015/12/15/medline KW - 18F-florbetapir KW - Alzheimer disease KW - PET KW - mild cognitive impairment KW - β-amyloid SP - 1351 EP - 8 JF - Journal of nuclear medicine : official publication, Society of Nuclear Medicine JO - J. Nucl. Med. VL - 56 IS - 9 N2 - UNLABELLED: Classification of subjects on the basis of amyloid PET scans is increasingly being used in research studies and clinical practice. Although qualitative, visual assessment is currently the gold standard approach, automated classification techniques are inherently more reproducible and efficient. The objective of this work was to develop a statistical approach for the automated classification of subjects with different levels of cognitive impairment into a group with low amyloid levels (AβL) and a group with high amyloid levels (AβH) through the use of amyloid PET data from the Alzheimer Disease Neuroimaging Initiative study. METHODS: In our framework, an iterative, voxelwise, regularized discriminant analysis is combined with a receiver operating characteristic approach that optimizes the selection of a region of interest (ROI) and a cutoff value for the automated classification of subjects into the AβL and AβH groups. The robustness, spatial stability, and generalization of the resulting target ROIs were evaluated by use of the standardized uptake value ratio for (18)F-florbetapir PET images from subjects who served as healthy controls, subjects who had mild cognitive impairment, and subjects who had Alzheimer disease and were participating in the Alzheimer Disease Neuroimaging Initiative study. RESULTS: We determined that several iterations of the discriminant analysis improved the classification of subjects into the AβL and AβH groups. We found that an ROI consisting of the posterior cingulate cortex/precuneus and the medial frontal cortex yielded optimal group separation and showed good stability across different reference regions and cognitive cohorts. A key step in this process was the automated determination of the cutoff value for group separation, which was dependent on the reference region used for the standardized uptake value ratio calculation and which was shown to have a relatively narrow range across subject groups. CONCLUSION: We developed a data-driven approach for the determination of an optimal target ROI and an associated cutoff value for the separation of subjects into the AβL and AβH groups. Future work should include the application of this process to other datasets to facilitate the determination of the translatability of the optimal ROI obtained in this study to other populations. Ideally, the accuracy of our target ROI and cutoff value could be further validated with PET-autopsy data from large-scale studies. It is anticipated that this approach will be extremely useful for the enrichment of study populations in clinical trials involving putative disease-modifying therapeutic agents for Alzheimer disease. SN - 1535-5667 UR - https://www.unboundmedicine.com/medline/citation/26135108/Optimal_Target_Region_for_Subject_Classification_on_the_Basis_of_Amyloid_PET_Images_ L2 - http://jnm.snmjournals.org/cgi/pmidlookup?view=long&pmid=26135108 DB - PRIME DP - Unbound Medicine ER -