Predicting progression of Alzheimer's disease using ordinal regression.
We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0105542 |
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| author | Orla M Doyle Eric Westman Andre F Marquand Patrizia Mecocci Bruno Vellas Magda Tsolaki Iwona Kłoszewska Hilkka Soininen Simon Lovestone Steve C R Williams Andrew Simmons |
| author_facet | Orla M Doyle Eric Westman Andre F Marquand Patrizia Mecocci Bruno Vellas Magda Tsolaki Iwona Kłoszewska Hilkka Soininen Simon Lovestone Steve C R Williams Andrew Simmons |
| author_sort | Orla M Doyle |
| collection | DOAJ |
| description | We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression--the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer's Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ = -0.64, ADNI and ρ = -0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12-24 months) and late converters (24-36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive. |
| format | Article |
| id | doaj-art-ec1a5bb91fe94a48a373320959306bc1 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ec1a5bb91fe94a48a373320959306bc12025-08-20T02:22:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0198e10554210.1371/journal.pone.0105542Predicting progression of Alzheimer's disease using ordinal regression.Orla M DoyleEric WestmanAndre F MarquandPatrizia MecocciBruno VellasMagda TsolakiIwona KłoszewskaHilkka SoininenSimon LovestoneSteve C R WilliamsAndrew SimmonsWe propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression--the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer's Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ = -0.64, ADNI and ρ = -0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12-24 months) and late converters (24-36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.https://doi.org/10.1371/journal.pone.0105542 |
| spellingShingle | Orla M Doyle Eric Westman Andre F Marquand Patrizia Mecocci Bruno Vellas Magda Tsolaki Iwona Kłoszewska Hilkka Soininen Simon Lovestone Steve C R Williams Andrew Simmons Predicting progression of Alzheimer's disease using ordinal regression. PLoS ONE |
| title | Predicting progression of Alzheimer's disease using ordinal regression. |
| title_full | Predicting progression of Alzheimer's disease using ordinal regression. |
| title_fullStr | Predicting progression of Alzheimer's disease using ordinal regression. |
| title_full_unstemmed | Predicting progression of Alzheimer's disease using ordinal regression. |
| title_short | Predicting progression of Alzheimer's disease using ordinal regression. |
| title_sort | predicting progression of alzheimer s disease using ordinal regression |
| url | https://doi.org/10.1371/journal.pone.0105542 |
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