Imaging‐based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment

Ithapu, Vamsi Krishna; Singh, Vikas; Okonkwo, Ozioma C.; Chappell, Richard J.; Dowling, N. Maritza; Johnson, Sterling C.

doi:10.1016/j.jalz.2015.01.010

Cited by 60 publications

(51 citation statements)

References 28 publications

(56 reference statements)

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“…However, a trial enrichment strategy using ApoE status would require caution because a recent study has reported that ApoE ɛ 4 carriers have a higher risk of amyloid-related imaging abnormalities than ApoE ɛ 4 non-carriers in clinical trials of immunotherapy for reducing cerebral amyloid burden using bapineuzumab [61]. For ApoE ɛ 4 non-carriers, on the other hand, other clinical enrichment strategies based on a machine learning method that handles data from imaging biomarkers such as those of MRI and/or PET could enrich clinical trials by enabling the selection of participants who will show future cognitive and neural decline [62]. …”

Section: Discussionmentioning

confidence: 99%

Sample Size Estimation for Alzheimer’s Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging

Fujishima

Kawaguchi

Maikusa

et al. 2017

JAD

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Background: Little is known about the sample sizes required for clinical trials of Alzheimer’s disease (AD)-modifying treatments using atrophy measures from serial brain magnetic resonance imaging (MRI) in the Japanese population.Objective: The primary objective of the present study was to estimate how large a sample size would be needed for future clinical trials for AD-modifying treatments in Japan using atrophy measures of the brain as a surrogate biomarker.Methods: Sample sizes were estimated from the rates of change of the whole brain and hippocampus by the k-means normalized boundary shift integral (KN-BSI) and cognitive measures using the data of 537 Japanese Alzheimer’s Neuroimaging Initiative (J-ADNI) participants with a linear mixed-effects model. We also examined the potential use of ApoE status as a trial enrichment strategy.Results: The hippocampal atrophy rate required smaller sample sizes than cognitive measures of AD and mild cognitive impairment (MCI). Inclusion of ApoE status reduced sample sizes for AD and MCI patients in the atrophy measures.Conclusion: These results show the potential use of longitudinal hippocampal atrophy measurement using automated image analysis as a progression biomarker and ApoE status as a trial enrichment strategy in a clinical trial of AD-modifying treatment in Japanese people.

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Section: Discussionmentioning

confidence: 99%

Sample Size Estimation for Alzheimer’s Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging

Fujishima

Kawaguchi

Maikusa

et al. 2017

JAD

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“…Suk et al (2014) also proposed a generative deep learning to integrate structural and functional patterns inherent in MRI and PET, respectively, by learning shared representations. Similarly, Ithapu et al (2015) also devised a deep learning algorithm called a randomized denoising auto-encoder marker to integrate multimodal data of PET and MRI. However, due to complex composition of non-linear patterns in deep learning, it still remains challenging to interpret the learned representations.…”

Section: Related Workmentioning

confidence: 99%

Deep ensemble learning of sparse regression models for brain disease diagnosis

Suk

Lee

Shen

2017

Medical Image Analysis

261

122

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Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer’s disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘ Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.

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“…Furthermore, classification frameworks can be used to develop imaging markers or indices (Davatzikos et al, 2008) with high sensitivity and specificity in individuals (Sajda, 2006) that can summarize the imaging profile of a subject into a single meaningful value (Habes et al, 2016b). This creates a more individualized, patient-tailored approach (Ithapu et al, 2015), which is imperative in the current age of personalized medicine because it allows further consideration of genetic or life-style risks, by utilizing advanced computational power (Habes et al, 2016a; Habes et al, 2016b; Habes et al, 2016c). …”

Section: Introductionmentioning

confidence: 99%

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

et al. 2017

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Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer’s disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD classification and mild cognitive impairment, selected after online database searches in Google Scholar and PubMed (January, 1985 to June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and address some future research directions.

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Imaging‐based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment

Cited by 60 publications

References 28 publications

Sample Size Estimation for Alzheimer’s Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging

Sample Size Estimation for Alzheimer’s Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging

Deep ensemble learning of sparse regression models for brain disease diagnosis

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

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