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BibTex format

author = {Tong, T and Gao, Q and Guerrero, R and Ledig, C and Chen, L and Rueckert, D},
doi = {10.1109/TBME.2016.2549363},
journal = {IEEE Transactions on Biomedical Engineering},
pages = {155--165},
title = {A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease},
url = {},
volume = {64},
year = {2016}

RIS format (EndNote, RefMan)

AB - OBJECTIVE: Identifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance (MR) images. METHODS: We propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we comprehensively study the effects of several important factors on the performance in the prediction task including registration accuracy, age correction, feature selection and the selection of training data. Based on the studies of these factors, a grading biomarker is then calculated for each MCI subject using sparse representation techniques. Finally, the grading biomarker is combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD conversion. RESULTS: Using the ADNI dataset, the proposed global grading biomarker achieved an area under the receiver operating characteristic curve (AUC) in the range of 79%-81% for the prediction of MCI-to-AD conversion within 3 years in 10-fold cross validations. The classification AUC further increases to 84%-92% when age and cognitive measures are combined with the proposed grading biomarker. CONCLUSION: The obtained accuracy of the proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space; the removal of the normal aging effect; selection of discriminative voxels; the calculation of the grading biomarker using AD and normal control groups; the integration of sparse representation technique and the combination of cognitive measures. SIGNIFICANCE: The evaluation on the ADNI dataset shows the efficacy of the proposed biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD conversion.
AU - Tong,T
AU - Gao,Q
AU - Guerrero,R
AU - Ledig,C
AU - Chen,L
AU - Rueckert,D
DO - 10.1109/TBME.2016.2549363
EP - 165
PY - 2016///
SN - 1558-2531
SP - 155
TI - A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease
T2 - IEEE Transactions on Biomedical Engineering
UR -
UR -
VL - 64
ER -