Document Type
Conference Paper
Publication Date
2022
DOI
10.1117/12.2613114
Publication Title
Proceedings of SPIE, Medical Imaging 2022: Computer-Aided Diagnosis
Volume
12033
Pages
120332I (1-7)
Conference Name
SPIE Medical Imaging, February 20- March 28, 2022, San Diego, California
Abstract
Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can accurately distinguish rBT from RN. In this work, sophisticated radiomic texture features are used to distinguish rBT from RN on multimodal MRI for disease characterization. First, stochastic multiresolution radiomic descriptor that captures voxel-level textural and structural heterogeneity as well as intensity and histogram features are extracted. Subsequently, these features are used in a machine learning setting to characterize the rBT from RN from four sequences of the MRI with 155 imaging slices for 30 GBM cases (12 RN, 18 rBT). To reduce the bias in accuracy estimation our model is implemented using Leave-one-out crossvalidation (LOOCV) and stratified 5-fold cross-validation with a Random Forest classifier. Our model offers mean accuracy of 0.967 ± 0.180 for LOOCV and 0.933 ± 0.082 for stratified 5-fold cross-validation using multiresolution texture features for discrimination of rBT from RN in this study. Our findings suggest that sophisticated texture feature may offer better discrimination between rBT and RN in MRI compared to other works in the literature.
Original Publication Citation
Sadique, M. S., Temtam, A., Lappinen, E., & Iftekharuddin, K. M. (2022). Radiomic texture feature descriptor to distinguish recurrent brain tumor from radiation necrosis using multimodal MRI. In K. Drukker, K.M. Iftekharuddin, H. Lu, M. Mazurowski, C. Muramatsu, R.K. Samala (Eds.), Proceedings of SPIE, Medical Imaging 2022: Computer-Aided Diagnosis, 120332I (1-7). https://doi.org/10.1117/12.2613114
Repository Citation
Sadique, M. S.; Temtam, A.; Lappinen, E.; and Iftekharuddin, K. M., "Radiomic Texture Feature Descriptor to Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal MRI" (2022). Electrical & Computer Engineering Faculty Publications. 329.
https://digitalcommons.odu.edu/ece_fac_pubs/329
ORCID
0000-0001-8316-4163 (Iftekharuddin)
Included in
Biomedical Commons, Neurology Commons, Oncology Commons, Radiology Commons
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.