Cancer treatment outcome prediction by assessing temporal change: application to cervical cancer

Jeffrey W. Prescott; Dongqing Zhang; Jian Z. Wang; Nina A. Mayr M.D.; William T. C. Yuh M.D.; Joel Saltz; Metin Gurcan

doi:10.1117/12.770867

1 April 2008 Cancer treatment outcome prediction by assessing temporal change: application to cervical cancer

Jeffrey W. Prescott, Dongqing Zhang, Jian Z. Wang, Nina A. Mayr M.D., William T. C. Yuh M.D., Joel Saltz, Metin Gurcan

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69152X (2008) https://doi.org/10.1117/12.770867
Event: Medical Imaging, 2008, San Diego, California, United States

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.

Citation Download Citation

Jeffrey W. Prescott, Dongqing Zhang, Jian Z. Wang, Nina A. Mayr M.D., William T. C. Yuh M.D., Joel Saltz, and Metin Gurcan "Cancer treatment outcome prediction by assessing temporal change: application to cervical cancer", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152X (1 April 2008); https://doi.org/10.1117/12.770867

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