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
