Next Article in Journal
Association between PD-1 and PD-L1 Polymorphisms and the Risk of Cancer: A Meta-Analysis of Case-Control Studies
Previous Article in Journal
ERCC1 Is a Predictor of Anthracycline Resistance and Taxane Sensitivity in Early Stage or Locally Advanced Breast Cancers
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle

Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma

1
Division of Radiation Oncology, Department of Oncology, McGill University, Montreal, QC H4A 3J1, Canada
2
The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
3
Department of Pathology, McGill University, Montreal, QC H4A 3J1, Canada
4
Research Institute of the McGill University Health Centre, Glen Site, Montreal, QC H4A 3J1, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2019, 11(8), 1148; https://doi.org/10.3390/cancers11081148
Received: 15 July 2019 / Revised: 5 August 2019 / Accepted: 8 August 2019 / Published: 10 August 2019
  |  
PDF [5145 KB, uploaded 10 August 2019]
  |  

Abstract

Predictors of patient outcome derived from gene methylation, mutation, or expression are severely limited in IDH1 wild-type glioblastoma (GBM). Radiomics offers an alternative insight into tumor characteristics which can provide complementary information for predictive models. The study aimed to evaluate whether predictive models which integrate radiomic, gene, and clinical (multi-omic) features together offer an increased capacity to predict patient outcome. A dataset comprising 200 IDH1 wild-type GBM patients, derived from The Cancer Imaging Archive (TCIA) (n = 71) and the McGill University Health Centre (n = 129), was used in this study. Radiomic features (n = 45) were extracted from tumor volumes then correlated to biological variables and clinical outcomes. By performing 10-fold cross-validation (n = 200) and utilizing independent training/testing datasets (n = 100/100), an integrative model was derived from multi-omic features and evaluated for predictive strength. Integrative models using a limited panel of radiomic (sum of squares variance, large zone/low gray emphasis, autocorrelation), clinical (therapy type, age), genetic (CIC, PIK3R1, FUBP1) and protein expression (p53, vimentin) yielded a maximal AUC of 78.24% (p = 2.9 × 10−5). We posit that multi-omic models using the limited set of ‘omic’ features outlined above can improve capacity to predict the outcome for IDH1 wild-type GBM patients. View Full-Text
Keywords: IDH1; radiomics; glioblastoma; survival time IDH1; radiomics; glioblastoma; survival time
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Chaddad, A.; Daniel, P.; Sabri, S.; Desrosiers, C.; Abdulkarim, B. Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma. Cancers 2019, 11, 1148.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Cancers EISSN 2072-6694 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top