Dr. Edward P. Lin Profile

Dr. Edward P. Lin

at Univ. of Rochester Medical Ctr.

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Proceedings Article | 15 March 2019 Presentation + Paper

Investigating a quantitative radiomics approach for brain tumor classification

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

Proceedings Volume 10953, 109530B (2019) https://doi.org/10.1117/12.2512995

KEYWORDS: Tumors, Brain, Magnetic resonance imaging, Machine learning, Feature extraction, Wavelets, Computer aided diagnosis and therapy

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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.

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