Multivariate Sparse Group Lasso Joint Model for Radiogenomics Data

TL;DR

This paper introduces a multivariate sparse group lasso joint model that effectively integrates imaging and genomic data to improve prediction of clinical outcomes in radiogenomics, accommodating different data types and leveraging group information.

Contribution

It proposes a novel joint modeling approach with weighted penalties that enhances feature selection across two related models in radiogenomics.

Findings

Outperforms existing methods in simulations

Effective with both continuous and time-to-event outcomes

Handles separate datasets for imaging and genomic data

Abstract

Radiogenomics is an emerging field in cancer research that combines medical imaging data with genomic data to predict patients clinical outcomes. In this paper, we propose a multivariate sparse group lasso joint model to integrate imaging and genomic data for building prediction models. Specifically, we jointly consider two models, one regresses imaging features on genomic features, and the other regresses patients clinical outcomes on genomic features. The regularization penalties through sparse group lasso allow incorporation of intrinsic group information, e.g. biological pathway and imaging category, to select both important intrinsic groups and important features within a group. To integrate information from the two models, in each model, we introduce a weight in the penalty term of each individual genomic feature, where the weight is inversely correlated with the model coefficient of that feature in the other model. This weight allows a feature to have a higher chance of selection by one model if it is selected by the other model. Our model is applicable to both continuous and time to event outcomes. It also allows the use of two separate datasets to fit the two models, addressing a practical challenge that many genomic datasets do not have imaging data available. Simulations and real data analyses demonstrate that our method outperforms existing methods in the literature.