Non-Binary Approaches for Classification of Amyloid Brain PET

Katherine Zukotvnski; Vincent C. Gaudet; Phillip Kuo; Sabrina Adamo; Maged Goubran; Christian Bocti; Michael Borrie; Howard Chertkow; Richard Frayne; Robin Hsiung; Robert Laforce; Michael D. Noseworthy; Frank S. Prato; Jim D. Sahlas; Christopher Scott; Eric E. Smith; Vesna Sossi; Alex Thiel; Jean Paul Soucy; Jean Claude Tardif; Sandra E. Black

doi:10.1109/ISMVL.2019.00043

Non-Binary Approaches for Classification of Amyloid Brain PET

Katherine Zukotvnski, Vincent C. Gaudet, Phillip Kuo, Sabrina Adamo, Maged Goubran, Christian Bocti, Michael Borrie, Howard Chertkow, Richard Frayne, Robin Hsiung, Robert Laforce, Michael D. Noseworthy, Frank S. Prato, Jim D. Sahlas, Christopher Scott, Eric E. Smith, Vesna Sossi, Alex Thiel, Jean Paul Soucy, Jean Claude TardifSandra E. Black

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Machine learning (ML) is increasingly used in medical imaging. This paper provides pilot data of decision trees and random forests (RFs) to predict if a ¹⁸F-florbetapir brain positron emission tomography (PET) is positive or negative for amyloid deposition based on quantitative data analysis. The dataset included 55 ¹⁸F-florbetapir brain PETs in participants with severe white matter disease and mild cognitive impairment (MCI), early Alzheimer's disease (AD) or transient ischemic events. The Montreal Cognitive Assessment (MoCA) score was known for each participant. All PET images were processed using the MINC toolkit to extract standardized uptake value ratios (SUVRs) for 59 regions of interest (features). Each PET was clinically read by 2 dual certified radiology/nuclear medicine physicians with final interpretation based on consensus. An initial study of RFs using conventional binary decision trees and PET quantitation suggests this is a powerful algorithm for PET classification as positive or negative for amyloid deposition. Preliminary data did not show improved results when a ternary RF approach was used. Finally, a soft-decision approach may be helpful to predict the MoCA score.

Original language	English (US)
Title of host publication	Proceedings - 2019 IEEE 49th International Symposium on Multiple-Valued Logic, ISMVL 2019
Publisher	IEEE Computer Society
Pages	206-211
Number of pages	6
ISBN (Electronic)	9781728100913
DOIs	https://doi.org/10.1109/ISMVL.2019.00043
State	Published - May 2019
Event	49th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2019 - Fredericton, Canada Duration: May 21 2019 → May 23 2019

Publication series

Name	Proceedings of The International Symposium on Multiple-Valued Logic
Volume	2019-May
ISSN (Print)	0195-623X

Conference

Conference	49th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2019
Country/Territory	Canada
City	Fredericton
Period	5/21/19 → 5/23/19

Keywords

brain imaging
decision tree
dementia
machine learning
multiple-valued logic
nuclear medicine
positron emission tomography
random forest

ASJC Scopus subject areas

Computer Science(all)
Mathematics(all)

Access to Document

10.1109/ISMVL.2019.00043

Cite this

Zukotvnski, K., Gaudet, V. C., Kuo, P., Adamo, S., Goubran, M., Bocti, C., Borrie, M., Chertkow, H., Frayne, R., Hsiung, R., Laforce, R., Noseworthy, M. D., Prato, F. S., Sahlas, J. D., Scott, C., Smith, E. E., Sossi, V., Thiel, A., Soucy, J. P., ... Black, S. E. (2019). Non-Binary Approaches for Classification of Amyloid Brain PET. In Proceedings - 2019 IEEE 49th International Symposium on Multiple-Valued Logic, ISMVL 2019 (pp. 206-211). [8758715] (Proceedings of The International Symposium on Multiple-Valued Logic; Vol. 2019-May). IEEE Computer Society. https://doi.org/10.1109/ISMVL.2019.00043

Non-Binary Approaches for Classification of Amyloid Brain PET. / Zukotvnski, Katherine; Gaudet, Vincent C.; Kuo, Phillip et al.
Proceedings - 2019 IEEE 49th International Symposium on Multiple-Valued Logic, ISMVL 2019. IEEE Computer Society, 2019. p. 206-211 8758715 (Proceedings of The International Symposium on Multiple-Valued Logic; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zukotvnski, K, Gaudet, VC, Kuo, P, Adamo, S, Goubran, M, Bocti, C, Borrie, M, Chertkow, H, Frayne, R, Hsiung, R, Laforce, R, Noseworthy, MD, Prato, FS, Sahlas, JD, Scott, C, Smith, EE, Sossi, V, Thiel, A, Soucy, JP, Tardif, JC & Black, SE 2019, Non-Binary Approaches for Classification of Amyloid Brain PET. in Proceedings - 2019 IEEE 49th International Symposium on Multiple-Valued Logic, ISMVL 2019., 8758715, Proceedings of The International Symposium on Multiple-Valued Logic, vol. 2019-May, IEEE Computer Society, pp. 206-211, 49th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2019, Fredericton, Canada, 5/21/19. https://doi.org/10.1109/ISMVL.2019.00043

Zukotvnski K, Gaudet VC, Kuo P, Adamo S, Goubran M, Bocti C et al. Non-Binary Approaches for Classification of Amyloid Brain PET. In Proceedings - 2019 IEEE 49th International Symposium on Multiple-Valued Logic, ISMVL 2019. IEEE Computer Society. 2019. p. 206-211. 8758715. (Proceedings of The International Symposium on Multiple-Valued Logic). doi: 10.1109/ISMVL.2019.00043

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abstract = "Machine learning (ML) is increasingly used in medical imaging. This paper provides pilot data of decision trees and random forests (RFs) to predict if a 18F-florbetapir brain positron emission tomography (PET) is positive or negative for amyloid deposition based on quantitative data analysis. The dataset included 55 18F-florbetapir brain PETs in participants with severe white matter disease and mild cognitive impairment (MCI), early Alzheimer's disease (AD) or transient ischemic events. The Montreal Cognitive Assessment (MoCA) score was known for each participant. All PET images were processed using the MINC toolkit to extract standardized uptake value ratios (SUVRs) for 59 regions of interest (features). Each PET was clinically read by 2 dual certified radiology/nuclear medicine physicians with final interpretation based on consensus. An initial study of RFs using conventional binary decision trees and PET quantitation suggests this is a powerful algorithm for PET classification as positive or negative for amyloid deposition. Preliminary data did not show improved results when a ternary RF approach was used. Finally, a soft-decision approach may be helpful to predict the MoCA score.",

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Non-Binary Approaches for Classification of Amyloid Brain PET

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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