Radiomic Features From Muti-Institutional Glioblastoma MRI Offers Additive Prognostic Value to Clinical and Genomic Markers

Authors

Spencer Liem, BS, Thomas Jefferson UniversityFollow
Gaurav Shukla, MD, PhD, Thomas Jefferson UniversityFollow
Spyridon Bakas, PhD, University of Pennsylvania
Sung Min Ha, MS, University of Pennsylvania
Saima Rathore, PhD, University of Pennsylvania
Christos Davatzikos, PhD, University of Pennsylvania

Document Type

Poster

Publication Date

4-10-2018

Abstract

Objective

Synergies between clinical, genomic, and radiomic features should improve the predictive value of each group of features and their combinations through a prognostic classifier based on machine learning in patients with glioblastoma.

Recommended Citation

Liem, BS, Spencer; Shukla, MD, PhD, Gaurav; Bakas, PhD, Spyridon; Ha, MS, Sung Min; Rathore, PhD, Saima; and Davatzikos, PhD, Christos, "Radiomic Features From Muti-Institutional Glioblastoma MRI Offers Additive Prognostic Value to Clinical and Genomic Markers" (2018). Sigma Xi Student Research Day. Paper 13.
https://jdc.jefferson.edu/sigmaxi/13

Language

English