Author ORCID Identifier
Date Available
5-11-2024
Year of Publication
2022
Degree Name
Doctor of Philosophy (PhD)
Document Type
Doctoral Dissertation
College
Arts and Sciences
Department/School/Program
Statistics
First Advisor
Dr. Chi Wang
Second Advisor
Dr. Arnold J. Stromberg
Abstract
Radiogenomics is a new direction in cancer research that focuses on the associations among radiomics, genomics and clinical outcome. Currently, the major challenge for Radiogenomics lies in the effective integration of genomics and imaging data for promising clinical outcome prediction. Herein, we propose a multivariate joint model that can integrate imaging and genomic data for better predicting the clinical outcome. Specifically, we jointly consider two multivariate group lasso models, one regresses imaging features on genomic features, and the other regresses patient’s clinical outcome on genomic features. An L1 penalty term is introduced for each variable, and weight in the penalty term is considered to allow features to have a higher chance of selection by one model if they are selected by the other model. In addition, we include an L2 penalty term in the model since genomic pathway and imaging types are incorporated as group information to improve feature selection. Subsequently, we implement an accelarated generalized coordinate descent algorithm for the joint model. We first build the model for radiogenomics data with continuous outcome, and then extend the model and algorithm to data with survival outcome. Our joint model allows the use of separate datasets to fit the two models, resolving the existing limitations in available datasets. As there are many public datasets only has genomic but not imaging information, our approach enables to utilize such datasets in combination with other datasets where imaging data is available, thereby to better predict the clinical outcome. Simulations and real data analyses demonstrate that our methods outperform the existing methods in the literature. Finally, an R package rgjoint for the proposed methods is developed and presented in this dissertation.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2022.153
Recommended Citation
Zeng, Tiantian, "Multivariate Statistical Modeling for Radio-genomics Study" (2022). Theses and Dissertations--Statistics. 61.
https://uknowledge.uky.edu/statistics_etds/61
