Using deep-learning to predict outcome of patients with Parkinson's disease

K. H. Leung, M. R. Salmanpour, A. Saberi, I. S. Klyuzhin, V. Sossi, A. K. Jha, Martin Gilbert Pomper, Yong Du, A. Rahmim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There are currently no established disease modifying therapies for PD, and prediction of outcome in PD to power clinical studies is a very important area of research. Assessment of PD is informed by imaging the dopamine system with dopamine transporter (DAT) single-photon emission computed tomography (SPECT) imaging and by the presence of key symptoms. Recently, deep-learning based methods have shown promise for medical image analysis tasks and disease detection. The purpose of this study was to develop a deep-learning based approach to predict outcome of patients with PD using longitudinal clinical data containing imaging and non-imaging information. Features were first extracted from the clinical data by the proposed deep-learning based approach and then combined to predict motor performance (MDS-UPDRS-III) in year 4. The performance of the proposed approach was evaluated via a 10-fold cross-validation. We evaluated the performance of the network on the basis of mean absolute error (MAE) between the predicted and true MDS-UPDRS part III scores in year 4. The proposed approach yielded a MAE of 4.33±3.36 when given only imaging features, 3.71±2.91 when given only non-imaging features, and 3.22±2.71 when given all input data. While the approach given only non-imaging input data outperformed the approach given only imaging data, we found that the performance of the proposed approach substantially improved when given both imaging and non-imaging information. Our results indicate that the addition of imaging data to non-imaging clinical data is helpful for the prediction of outcome in patients with PD. The proposed approach that incorporated both imaging and non-imaging clinical data shows significant promise for prediction of outcome in patients with PD.

Original languageEnglish (US)
Title of host publication2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538684948
DOIs
StatePublished - Nov 1 2018
Event2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Sydney, Australia
Duration: Nov 10 2018Nov 17 2018

Publication series

Name2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings

Conference

Conference2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018
CountryAustralia
CitySydney
Period11/10/1811/17/18

Fingerprint

Parkinson disease
learning
Parkinson Disease
Learning
dopamine
Dopamine Plasma Membrane Transport Proteins
predictions
Single-Photon Emission-Computed Tomography
transporter
Dopamine
image analysis
therapy
tomography
Research
photons
Therapeutics

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Nuclear and High Energy Physics

Cite this

Leung, K. H., Salmanpour, M. R., Saberi, A., Klyuzhin, I. S., Sossi, V., Jha, A. K., ... Rahmim, A. (2018). Using deep-learning to predict outcome of patients with Parkinson's disease. In 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings [8824432] (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NSSMIC.2018.8824432

Using deep-learning to predict outcome of patients with Parkinson's disease. / Leung, K. H.; Salmanpour, M. R.; Saberi, A.; Klyuzhin, I. S.; Sossi, V.; Jha, A. K.; Pomper, Martin Gilbert; Du, Yong; Rahmim, A.

2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8824432 (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Leung, KH, Salmanpour, MR, Saberi, A, Klyuzhin, IS, Sossi, V, Jha, AK, Pomper, MG, Du, Y & Rahmim, A 2018, Using deep-learning to predict outcome of patients with Parkinson's disease. in 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings., 8824432, 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018, Sydney, Australia, 11/10/18. https://doi.org/10.1109/NSSMIC.2018.8824432
Leung KH, Salmanpour MR, Saberi A, Klyuzhin IS, Sossi V, Jha AK et al. Using deep-learning to predict outcome of patients with Parkinson's disease. In 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8824432. (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings). https://doi.org/10.1109/NSSMIC.2018.8824432
Leung, K. H. ; Salmanpour, M. R. ; Saberi, A. ; Klyuzhin, I. S. ; Sossi, V. ; Jha, A. K. ; Pomper, Martin Gilbert ; Du, Yong ; Rahmim, A. / Using deep-learning to predict outcome of patients with Parkinson's disease. 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings).
@inproceedings{f746d1239955413e9d51a846f5497109,
title = "Using deep-learning to predict outcome of patients with Parkinson's disease",
abstract = "There are currently no established disease modifying therapies for PD, and prediction of outcome in PD to power clinical studies is a very important area of research. Assessment of PD is informed by imaging the dopamine system with dopamine transporter (DAT) single-photon emission computed tomography (SPECT) imaging and by the presence of key symptoms. Recently, deep-learning based methods have shown promise for medical image analysis tasks and disease detection. The purpose of this study was to develop a deep-learning based approach to predict outcome of patients with PD using longitudinal clinical data containing imaging and non-imaging information. Features were first extracted from the clinical data by the proposed deep-learning based approach and then combined to predict motor performance (MDS-UPDRS-III) in year 4. The performance of the proposed approach was evaluated via a 10-fold cross-validation. We evaluated the performance of the network on the basis of mean absolute error (MAE) between the predicted and true MDS-UPDRS part III scores in year 4. The proposed approach yielded a MAE of 4.33±3.36 when given only imaging features, 3.71±2.91 when given only non-imaging features, and 3.22±2.71 when given all input data. While the approach given only non-imaging input data outperformed the approach given only imaging data, we found that the performance of the proposed approach substantially improved when given both imaging and non-imaging information. Our results indicate that the addition of imaging data to non-imaging clinical data is helpful for the prediction of outcome in patients with PD. The proposed approach that incorporated both imaging and non-imaging clinical data shows significant promise for prediction of outcome in patients with PD.",
author = "Leung, {K. H.} and Salmanpour, {M. R.} and A. Saberi and Klyuzhin, {I. S.} and V. Sossi and Jha, {A. K.} and Pomper, {Martin Gilbert} and Yong Du and A. Rahmim",
year = "2018",
month = "11",
day = "1",
doi = "10.1109/NSSMIC.2018.8824432",
language = "English (US)",
series = "2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings",

}

TY - GEN

T1 - Using deep-learning to predict outcome of patients with Parkinson's disease

AU - Leung, K. H.

AU - Salmanpour, M. R.

AU - Saberi, A.

AU - Klyuzhin, I. S.

AU - Sossi, V.

AU - Jha, A. K.

AU - Pomper, Martin Gilbert

AU - Du, Yong

AU - Rahmim, A.

PY - 2018/11/1

Y1 - 2018/11/1

N2 - There are currently no established disease modifying therapies for PD, and prediction of outcome in PD to power clinical studies is a very important area of research. Assessment of PD is informed by imaging the dopamine system with dopamine transporter (DAT) single-photon emission computed tomography (SPECT) imaging and by the presence of key symptoms. Recently, deep-learning based methods have shown promise for medical image analysis tasks and disease detection. The purpose of this study was to develop a deep-learning based approach to predict outcome of patients with PD using longitudinal clinical data containing imaging and non-imaging information. Features were first extracted from the clinical data by the proposed deep-learning based approach and then combined to predict motor performance (MDS-UPDRS-III) in year 4. The performance of the proposed approach was evaluated via a 10-fold cross-validation. We evaluated the performance of the network on the basis of mean absolute error (MAE) between the predicted and true MDS-UPDRS part III scores in year 4. The proposed approach yielded a MAE of 4.33±3.36 when given only imaging features, 3.71±2.91 when given only non-imaging features, and 3.22±2.71 when given all input data. While the approach given only non-imaging input data outperformed the approach given only imaging data, we found that the performance of the proposed approach substantially improved when given both imaging and non-imaging information. Our results indicate that the addition of imaging data to non-imaging clinical data is helpful for the prediction of outcome in patients with PD. The proposed approach that incorporated both imaging and non-imaging clinical data shows significant promise for prediction of outcome in patients with PD.

AB - There are currently no established disease modifying therapies for PD, and prediction of outcome in PD to power clinical studies is a very important area of research. Assessment of PD is informed by imaging the dopamine system with dopamine transporter (DAT) single-photon emission computed tomography (SPECT) imaging and by the presence of key symptoms. Recently, deep-learning based methods have shown promise for medical image analysis tasks and disease detection. The purpose of this study was to develop a deep-learning based approach to predict outcome of patients with PD using longitudinal clinical data containing imaging and non-imaging information. Features were first extracted from the clinical data by the proposed deep-learning based approach and then combined to predict motor performance (MDS-UPDRS-III) in year 4. The performance of the proposed approach was evaluated via a 10-fold cross-validation. We evaluated the performance of the network on the basis of mean absolute error (MAE) between the predicted and true MDS-UPDRS part III scores in year 4. The proposed approach yielded a MAE of 4.33±3.36 when given only imaging features, 3.71±2.91 when given only non-imaging features, and 3.22±2.71 when given all input data. While the approach given only non-imaging input data outperformed the approach given only imaging data, we found that the performance of the proposed approach substantially improved when given both imaging and non-imaging information. Our results indicate that the addition of imaging data to non-imaging clinical data is helpful for the prediction of outcome in patients with PD. The proposed approach that incorporated both imaging and non-imaging clinical data shows significant promise for prediction of outcome in patients with PD.

UR - http://www.scopus.com/inward/record.url?scp=85073111228&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073111228&partnerID=8YFLogxK

U2 - 10.1109/NSSMIC.2018.8824432

DO - 10.1109/NSSMIC.2018.8824432

M3 - Conference contribution

AN - SCOPUS:85073111228

T3 - 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings

BT - 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -