Ms. Tien T. Tang Profile

Tien T. Tang

PhD Student

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Proceedings Article | 27 February 2018 Paper

Classification of brain tumors using texture based analysis of T1-post contrast MR scans in a preclinical model

Tien Tang, Janice Zawaski, Kathleen Francis, Amina Qutub, M. Waleed Gaber

Proceedings Volume 10575, 105753T (2018) https://doi.org/10.1117/12.2293636

KEYWORDS: Tumors, Performance modeling, Image classification, Magnetic resonance imaging, Brain, Process modeling, Animal model studies, Neuroimaging, Statistical analysis, Feature extraction

Read Abstract +

Accurate diagnosis of tumor type is vital for effective treatment planning. Diagnosis relies heavily on tumor biopsies and other clinical factors. However, biopsies do not fully capture the tumor’s heterogeneity due to sampling bias and are only performed if the tumor is accessible. An alternative approach is to use features derived from routine diagnostic imaging such as magnetic resonance (MR) imaging. In this study we aim to establish the use of quantitative image features to classify brain tumors and extend the use of MR images beyond tumor detection and localization. To control for interscanner, acquisition and reconstruction protocol variations, the established workflow was performed in a preclinical model. Using glioma (U87 and GL261) and medulloblastoma (Daoy) models, T1-weighted post contrast scans were acquired at different time points post-implant. The tumor regions at the center, middle, and peripheral were analyzed using in-house software to extract 32 different image features consisting of first and second order features. The extracted features were used to construct a decision tree, which could predict tumor type with 10-fold cross-validation. Results from the final classification model demonstrated that middle tumor region had the highest overall accuracy at 79%, while the AUC accuracy was over 90% for GL261 and U87 tumors. Our analysis further identified image features that were unique to certain tumor region, although GL261 tumors were more homogenous with no significant differences between the central and peripheral tumor regions. In conclusion our study shows that texture features derived from MR scans can be used to classify tumor type with high success rates. Furthermore, the algorithm we have developed can be implemented with any imaging datasets and may be applicable to multiple tumor types to determine diagnosis.

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