Document Type

Conference Paper

Publication Date

2022

DOI

10.1117/12.2612621

Publication Title

Proceedings of SPIE, Medical Imaging 2022: Computer-Aided Diagnosis

Volume

12033

Pages

120331E (1-7)

Conference Name

Medical Imaging 2022: Computer-Aided Diagnosis, February 20- March 27, 2022, San Diego, California

Abstract

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework for MGMT classification with uncertainty analysis utilizing imaging features extracted from multimodal magnetic resonance imaging (mMRI). The imaging features include conventional texture, volumetric, and sophisticated fractal, and multi-resolution fractal texture features. The proposed method is evaluated with publicly available BraTS-TCIA-GBM pre-operative scans and TCGA datasets with 114 patients. The experiment with 10-fold cross-validation suggests that the fractal and multi-resolution fractal texture features offer an improved prediction of MGMT status. The uncertainty analysis using an ensemble of Stochastic Gradient Langevin Boosting models along with multi-resolution fractal features offers an accuracy of 71.74% and area under the curve of 0.76. Finally, analysis shows that our proposed method with uncertainty analysis offers improved predictive performance when compared with different well-known methods in the literature.

Comments

Copyright 2022 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

Original Publication Citation

Farzana, W., Shboul, Z. A., Temtam, A., & Iftekharuddin, K. M. (2022). Uncertainty estimation in classification of MGMT using radiogenomics for glioblastoma patients. In K. Drukker, K.M. Iftekharuddin, H. Lu, M.A. Mazurowski, C. Muramatsu, R.K. Samala (Eds.), Proceedings of SPIE, Medical Imaging 2022: Computer-Aided Diagnosis 120331E (1-7). Society of Photo‑Optical Instrumentation Engineers. https://doi.org/10.1117/12.2612621

ORCID

0000-0001-8316-4163 (Iftekharuddin)

Download

Share

COinS