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Wavelet-Based Temporal Feature Extraction From DCE-MRI to Identify Sub-Volumes of Low Blood Volume in Head-And-Neck Cancer

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D You

D You*, M Aryal , S Samuels , A Eisbruch , Y Cao , University of Michigan, Ann Arbor, MI


SU-E-J-241 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose:A previous study showed that large sub-volumes of tumor with low blood volume (BV) (poorly perfused) in head-and-neck (HN) cancers are significantly associated with local-regional failure (LRF) after chemoradiation therapy, and could be targeted with intensified radiation doses. This study aimed to develop an automated and scalable model to extract voxel-wise contrast-enhanced temporal features of dynamic contrast-enhanced (DCE) MRI in HN cancers for predicting LRF.

Methods:Our model development consists of training and testing stages. The training stage includes preprocessing of individual-voxel DCE curves from tumors for intensity normalization and temporal alignment, temporal feature extraction from the curves, feature selection, and training classifiers. For feature extraction, multiresolution Haar discrete wavelet transformation is applied to each DCE curve to capture temporal contrast-enhanced features. The wavelet coefficients as feature vectors are selected. Support vector machine classifiers are trained to classify tumor voxels having either low or high BV, for which a BV threshold of 7.6% is previously established and used as ground truth. The model is tested by a new dataset. The voxel-wise DCE curves for training and testing were from 14 and 8 patients, respectively. A posterior probability map of the low BV class was created to examine the tumor sub-volume classification. Voxel-wise classification accuracy was computed to evaluate performance of the model.

Results:Average classification accuracies were 87.2% for training (10-fold cross-validation) and 82.5% for testing. The lowest and highest accuracies (patient-wise) were 68.7% and 96.4%, respectively. Posterior probability maps of the low BV class showed the sub-volumes extracted by our model similar to ones defined by the BV maps with most misclassifications occurred near the sub-volume boundaries.

Conclusion:This model could be valuable to support adaptive clinical trials with further validation. The framework could be extendable and scalable to extract temporal contrast-enhanced features of DCE-MRI in other tumors.

Funding Support, Disclosures, and Conflict of Interest: We would like to acknowledge NIH for funding support: UO1 CA183848

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