A comparison of feature selection methods for the development of a prognostic radiogenomic biomarker in Non-Small Cell Lung Cancer patients
In person: 23 February 2022 • 5:30 PM - 7:00 PM PST
Studies that involve radiomic or genomic feature descriptors of tumor regions usually incorporate some feature selection method to reduce their high-dimensional descriptors and ensure low collinearity among the features. However, it is important to explore various feature selection methods to identify optimal radiogenomic feature sets. These sets will be used to develop prognostic radiogenomic phenotypes. In this study, we explore three methods to select optimal radiomic and genomic features and identify statistically significant radiogenomic phenotypes. These phenotypes are subsequently integrated with stage, sex and histology in a multi-variate Cox proportional hazards model to predict overall survival in 85 NSCLC patients. The prognostic performance of the multi-variate models derived from the three methods is compared to evaluate the efficacy of the methods used to select optimal multi-modal features.
Univ. of Pennsylvania (United States)
Apurva Singh is a PhD student in the Department of Bioengineering at the University of Pennsylvania. She received her BTech in electronics and communication engineering from Manipal Academy of Higher Education, Manipal, India, and her MS degree in electrical and computer engineering from George Washington University, Washington D.C. Her current research interests focus on the development of novel radiogenomic biomarkers to characterize intratumor heterogeneity for improved prognosis in lung cancer patients.