TY - GEN
T1 - ADMultiImg
T2 - Medical Imaging 2018: Computer-Aided Diagnosis
AU - Liu, Xiaonan
AU - Chen, Kewei
AU - Wu, Teresa
AU - Weidman, David
AU - Lure, Fleming
AU - Li, Jing
N1 - Funding Information:
This research is supported by the National Institute on Aging of the National Institutes of Health under award number R41AG053149.
Funding Information:
This research is supported by the National Institute on Aging of the National Institutes of Health under award number R41AG053149. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Also, all authors have read the journal's policy on disclosure of potential conflicts of interest. The authors do not have anything to disclose.
Publisher Copyright:
© 2018 SPIE.
PY - 2018
Y1 - 2018
N2 - Alzheimer's Disease (AD) is the most common cause of dementia and currently has no cure. Treatments targeting early stages of AD such as Mild Cognitive Impairment (MCI) may be most effective to deaccelerate AD, thus attracting increasing attention. However, MCI has substantial heterogeneity in that it can be caused by various underlying conditions, not only AD. To detect MCI due to AD, NIA-AA published updated consensus criteria in 2011, in which the use of multi-modality images was highlighted as one of the most promising methods. It is of great interest to develop a CAD system based on automatic, quantitative analysis of multi-modality images and machine learning algorithms to help physicians more adequately diagnose MCI due to AD. The challenge, however, is that multi-modality images are not universally available for many patients due to cost, access, safety, and lack of consent. We developed a novel Missing Modality Transfer Learning (MMTL) algorithm capable of utilizing whatever imaging modalities are available for an MCI patient to diagnose the patient's likelihood of MCI due to AD. Furthermore, we integrated MMTL with radiomics steps including image processing, feature extraction, and feature screening, and a post-processing for uncertainty quantification (UQ), and developed a CAD system called "ADMultiImg" to assist clinical diagnosis of MCI due to AD using multi-modality images together with patient demographic and genetic information. Tested on ADNI date, our system can generate a diagnosis with high accuracy even for patients with only partially available image modalities (AUC=0.94), and therefore may have broad clinical utility.
AB - Alzheimer's Disease (AD) is the most common cause of dementia and currently has no cure. Treatments targeting early stages of AD such as Mild Cognitive Impairment (MCI) may be most effective to deaccelerate AD, thus attracting increasing attention. However, MCI has substantial heterogeneity in that it can be caused by various underlying conditions, not only AD. To detect MCI due to AD, NIA-AA published updated consensus criteria in 2011, in which the use of multi-modality images was highlighted as one of the most promising methods. It is of great interest to develop a CAD system based on automatic, quantitative analysis of multi-modality images and machine learning algorithms to help physicians more adequately diagnose MCI due to AD. The challenge, however, is that multi-modality images are not universally available for many patients due to cost, access, safety, and lack of consent. We developed a novel Missing Modality Transfer Learning (MMTL) algorithm capable of utilizing whatever imaging modalities are available for an MCI patient to diagnose the patient's likelihood of MCI due to AD. Furthermore, we integrated MMTL with radiomics steps including image processing, feature extraction, and feature screening, and a post-processing for uncertainty quantification (UQ), and developed a CAD system called "ADMultiImg" to assist clinical diagnosis of MCI due to AD using multi-modality images together with patient demographic and genetic information. Tested on ADNI date, our system can generate a diagnosis with high accuracy even for patients with only partially available image modalities (AUC=0.94), and therefore may have broad clinical utility.
KW - ADNI
KW - Alzheimer's Disease
KW - Mild Cognitive Impairment
KW - Missing Modality Transfer Learning
KW - NIA-AA criteria
KW - Uncertainty Quantification
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UR - http://www.scopus.com/inward/citedby.url?scp=85046269705&partnerID=8YFLogxK
U2 - 10.1117/12.2293698
DO - 10.1117/12.2293698
M3 - Conference contribution
AN - SCOPUS:85046269705
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2018
A2 - Mori, Kensaku
A2 - Petrick, Nicholas
PB - SPIE
Y2 - 12 February 2018 through 15 February 2018
ER -