Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols

Spyridon Bakas, Gaurav Shukla, Hamed Akbari, Guray Erus, Aristeidis Sotiras, Saima Rathore, Chiharu Sako, Sung Min Ha, Martin Rozycki, Ashish Singh, Russell Shinohara, Michel Bilello, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Glioblastoma, the most common and aggressive adult brain tumor, is considered non-curative at diagnosis. Current literature shows promise on imaging-based overall survival prediction for patients with glioblastoma while integrating advanced (structural, perfusion, and diffusion) multiparametric magnetic resonance imaging (AdvmpMRI). However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (BasmpMRI, i.e., T1,T1-Gd,T2,T2-FLAIR) pre-operatively, rather than Adv-mpMRI. Here we assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP) extracted from Adv-mpMRI can yield accurate overall survival stratification. We further focus on demonstrating that equally accurate prediction models can be constructed using augmented feature panels (AFP) extracted solely from Bas-mpMRI, obviating the need for using Adv-mpMRI. The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77%, and improved to 74.26% when utilizing the AFP on Bas-mpMRI. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using AFP on Basic-mpMRI. This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible by using solely Bas-mpMRI and integrative radiomic analysis can compensate for the lack of Adv-mpMRI. Our finding holds promise for predicting overall survival based on commonly-acquired Bas-mpMRI, and hence for potential generalization across multiple institutions that may not have access to Adv-mpMRI, facilitating better patient selection.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
PublisherSPIE
ISBN (Electronic)9781510633971
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11315
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceMedical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Country/TerritoryUnited States
CityHouston
Period02/16/2002/19/20

Keywords

  • Glioblastoma
  • Multivariate
  • Prediction
  • Prognosis
  • Radiomics
  • Survival

Fingerprint

Dive into the research topics of 'Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols'. Together they form a unique fingerprint.

Cite this