Investigating a quantitative radiomics approach for brain tumor classification

Anas Zainul Abidin; Irfaan Dar; Adora M. D'Souza; Edward P. Lin; Axel Wismüller

doi:10.1117/12.2512995

15 March 2019 Investigating a quantitative radiomics approach for brain tumor classification

Anas Zainul Abidin, Irfaan Dar, Adora M. D'Souza, Edward P. Lin, Axel Wismüller

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109530B (2019) https://doi.org/10.1117/12.2512995
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Differentiating a solitary brain metastasis (METS) from glioblastoma multiforme (GBM) is an important yet difficult task using current MR imaging techniques. A final diagnosis is obtained by performing a stereotactic brain biopsy, which carries a small but not insignificant risk. Distinguishing between primary and secondary malignant neoplasms is critical for providing appropriate patient prognosis, management and treatment planning. Devising non-invasive means of distinguishing between the two would be clinically useful, as patients may forego a surgical biopsy. In this study, we propose a radiomics approach for enhanced characterization and classification of such tumors. The final dataset for this study consisted of pre-treatment MRI scans acquired from 52 patients (aged 61 +/- 7 years; 31M, 21F; 35 GBM, 17 METS, 3T MRI scanner) consisting of Contrast-Enhanced (CE) T1 and T2 FLAIR sequences. The extracted features were used with an Adaboost classifier with a 10-fold cross validation scheme. The best results (AUC=0.84) were obtained using Local Binary Patterns extracted from the CE T1 sequences and also a high-dimensional feature vector from the wavelet-transformed image at the lowest frequency (AUC=0.84). Combining all the features from both the sequences resulted in a classification performance of 0.71. Our results suggest that radiomics-based machine learning analysis can accurately differentiate glioblastomas from metastatic brain tumors. Improvements in classification of such tumors could potentially reduce the need for an invasive stereotactic brain biopsy.

Conference Presentation

Citation Download Citation

Anas Zainul Abidin, Irfaan Dar, Adora M. D'Souza, Edward P. Lin, and Axel Wismüller "Investigating a quantitative radiomics approach for brain tumor classification", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530B (15 March 2019); https://doi.org/10.1117/12.2512995

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