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

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.