Hongming Li1, Mohamad Habes2, David A Wolk3, Yong Fan4. 1. Section for Biomedical Image Analysis (SBIA), Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 2. Section for Biomedical Image Analysis (SBIA), Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Cognitive Neurology Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 3. Cognitive Neurology Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 4. Section for Biomedical Image Analysis (SBIA), Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: yong.fan@uphs.upenn.edu.
Abstract
INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia. METHODS: A deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting. RESULTS: The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning-based progression risk was combined with baseline clinical measures. DISCUSSION: Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.
INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia. METHODS: A deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting. RESULTS: The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning-based progression risk was combined with baseline clinical measures. DISCUSSION: Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.
Authors: Sarah B Martin; Charles D Smith; Heather R Collins; Fred A Schmitt; Brian T Gold Journal: Neurobiol Aging Date: 2008-09-21 Impact factor: 4.673
Authors: Y W Lui; P D Chang; G Zaharchuk; D P Barboriak; A E Flanders; M Wintermark; C P Hess; C G Filippi Journal: AJNR Am J Neuroradiol Date: 2020-07-30 Impact factor: 3.825