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Multilevel Survival Analysis with Structured Penalties for Imaging Genetics data

Pascal Lu 1 Olivier Colliot 1
1 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : Predicting the future occurrence of Alzheimer's disease (AD) in patients with mild cognitive impairment (MCI) is a topic of active research. Many papers have formulated this question as a classification problem: one considers a fixed time of conversion and aims to discriminate between the patients who have converted to AD at that time and those who have not. However, a clinically more relevant question is to predict the date at which a patient to AD. Survival analysis is an adequate statistical framework for such a task. Multimodal data (imaging and genetic) provide complementary information for the prediction. While imaging data provides an estimate of the current patient's state, genetic variants can be associated to the speed of progression to AD. Although they do not provide the same type of information, most papers in classification or regression put imaging and genetic variables on the same level in order to predict the current or future patient's state. In this work, we propose a survival model using multimodal data to estimate the conversion date to AD, by considering joint effects between the imaging and genetic modalities. We introduce an adapted penalty in the survival model, the group lasso penalty, over joint groups of genes and brain regions. The model is evaluated on genetic (single nucleotide polymorphisms) and imaging (anatomical MRI measures) data from the ADNI database, and compared to a standard Cox model.
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https://hal.inria.fr/hal-02473825
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Submitted on : Tuesday, February 11, 2020 - 12:59:34 AM
Last modification on : Monday, April 5, 2021 - 5:12:02 PM
Long-term archiving on: : Tuesday, May 12, 2020 - 12:30:47 PM

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  • HAL Id : hal-02473825, version 1

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Pascal Lu, Olivier Colliot. Multilevel Survival Analysis with Structured Penalties for Imaging Genetics data. SPIE Medical Imaging Conference, Feb 2020, Houston, United States. ⟨hal-02473825⟩

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