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Proceedings Paper

Cancer treatment outcome prediction by assessing temporal change: application to cervical cancer
Author(s): Jeffrey W. Prescott; Dongqing Zhang; Jian Z. Wang; Nina A. Mayr; William T. C. Yuh; Joel Saltz; Metin Gurcan
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Paper Abstract

In this paper a novel framework is proposed for the classification of cervical tumors as susceptible or resistant to radiation therapy. The classification is based on both small- and large-scale temporal changes in the tumors' magnetic resonance imaging (MRI) response. The dataset consists of 11 patients who underwent radiation therapy for advanced cervical cancer. Each patient had dynamic contrast-enhanced (DCE)-MRI studies before treatment and early into treatment, approximately 2 weeks apart. For each study, a T1-weighted scan was performed before injection of contrast agent and again 75 seconds after injection. Using the two studies and the two series from each study, a set of tumor region of interest (ROI) features were calculated. These features were then exhaustively searched for the most separable set of three features based on a treatment outcome of local control or local recurrence. The dimensionality of the three-feature set was then reduced to two dimensions using principal components analysis (PCA). Finally, the classification performance was tested using three different classification procedures: support vector machines (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN). The most discriminatory features were those of volume, standard deviation, skewness, kurtosis, and fractal dimension. Combinations of these features resulted in 100% classification accuracy using each of the three classifiers.

Paper Details

Date Published: 1 April 2008
PDF: 11 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152X (1 April 2008); doi: 10.1117/12.770867
Show Author Affiliations
Jeffrey W. Prescott, The Ohio State Univ. (United States)
Dongqing Zhang, The Ohio State Univ. Medical Ctr. (United States)
Jian Z. Wang, The Ohio State Univ. Medical Ctr. (United States)
Nina A. Mayr, The Ohio State Univ. Medical Ctr. (United States)
William T. C. Yuh, The Ohio State Univ. Medical Ctr. (United States)
Joel Saltz, The Ohio State Univ. (United States)
Metin Gurcan, The Ohio State Univ. (United States)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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