Alzheimer’s Disease (AD) is a devastating neurodegenerative disease. Recent advances in tau-positron emission tomography (PET) imaging allow quantitating and mapping out the regional distribution of one important hallmark of AD across the brain. There is a need to develop machine learning (ML) algorithms to interrogate the utility of this new imaging modality. While there are some recent studies showing promise of using ML to differentiate AD patients from normal controls (NC) based on tau-PET images, there is limited work to investigate if tau-PET, with the help of ML, can facilitate predicting the risk of converting to AD while an individual is still at the early Mild Cognitive Impairment (MCI) stage. We developed an early AD risk predictor for subjects with MCI based on tau-PET using Machine Learning (ML). Our ML algorithms achieved good accuracy in predicting the risk of conversion to AD for a given MCI subject. Important features contributing to the prediction are consistent with literature reports of tau susceptible regions. This work demonstrated the feasibility of developing an early AD risk predictor for subjects with MCI based on tau-PET and ML.
Multi-modality images usually exist for diagnosis/prognosis of a disease, such as Alzheimer’s Disease (AD), but with different levels of accessibility and accuracy. MRI is used in the standard of care, thus having high accessibility to patients. On the other hand, imaging of pathologic hallmarks of AD such as amyloid-PET and tau-PET has low accessibility due to cost and other practical constraints, even though they are expected to provide higher diagnostic/prognostic accuracy than standard clinical MRI. We proposed Cross-Modality Transfer Learning (CMTL) for accurate diagnosis/prognosis based on standard imaging modality with high accessibility (mod_HA), with a novel training strategy of using not only data of mod_HA but also knowledge transferred from the model based on advanced imaging modality with low accessibility (mod_LA). We applied CMTL to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets, demonstrating improved performance of the MRI (mod_HA)-based model by leveraging the knowledge transferred from the model based on tau-PET (mod_LA).
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.
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