bibtext: @InProceedings{ Zha_Disease_MICCAI2024,
                   author = { Zhang, Jin and Shang, Muheng and Yang, Yan and Guo, Lei and Han, Junwei and Du, Lei },
                   title = { { Disease Progression Prediction Incorporating Genotype-Environment Interactions: A Longitudinal Neurodegenerative Disorder Study } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: Disease progression prediction is a fundamental yet challenging task in neurodegenerative disorders. Despite extensive research endeavors, disease progression fitting on brain imaging data alone may yield suboptimal performance due to the effect of potential interactions between genetic variations, proteomic expressions, and environmental exposures on the disease progression. To fill this gap, we draw on the idea of mutual-assistance (MA) learning and accordingly propose a fresh and powerful scheme, referred to as Mutual-Assistance Disease Progression fitting and Genotype-by-Environment interaction identification approach (MA-DPxGE). Specifically, our model jointly performs disease progression fitting using longitudinal imaging phenotypes and identification of genotype-by-environment interaction factors. To ensure stability and interpretability, we employ innovative penalties to discern significant risk factors. Moreover, we meticulously design adaptive mechanisms for loss-term reweighting, ensuring fair adjustments for each prediction task. Furthermore, due to high-dimensional genotype-by-environment interactions, we devise a rapid and efficient strategy to reduce runtime, ensuring practical availability and applicability. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset reveal that MA-DPxGE demonstrates superior performance compared to state-of-the-art approaches while maintaining exceptional interpretability. This outcome is pivotal in elucidating disease progression patterns and establishing effective strategies to mitigate or halt disease advancement.
title: Disease Progression Prediction Incorporating Genotype-Environment Interactions: A Longitudinal Neurodegenerative Disorder Study
authors: Zhang, Jin Shang, Muheng Yang, Yan Guo, Lei Han, Junwei Du, Lei