Multilevel survival analysis with structured penalties for imaging genetics data

Pascal Lu; Olivier Colliot

doi:10.1117/12.2549010

10 March 2020 Multilevel survival analysis with structured penalties for imaging genetics data

Pascal Lu, Olivier Colliot

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113130K (2020) https://doi.org/10.1117/12.2549010
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Pascal Lu and Olivier Colliot "Multilevel survival analysis with structured penalties for imaging genetics data", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130K (10 March 2020); https://doi.org/10.1117/12.2549010

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