Feature Extraction Optimized For Prostate Lesion Classification

In this paper we propose a method, which supports determination of clinical significance of prostate lesions. The goal was to differentiate clinical significance of abnormalities according to the indicated lesion location. The clinical context of the reported research is based on multiparemetric Magnetic Resonanse Imaging (mpMRI) obtained from the PROSTATEx SPIE challenge database. The PROSTATEx training data set contains 203 mpMRI cases with 330 lesions located in various prostate zones. The proposed method is based on textural and statistical features of imaged tissue extracted from normalized T2-weighted (transaxial, sagittal and coronal), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) and K-trans modalities. In addition patients' characteristics (age, weight, height) and lesion location were used. The impact of the modeled regions of interests of dimensions (2D or 3D) and size was also investigated. Standard classification method (K-nearest neighbors) has been applied in order to determine the most discriminative set of image and clinical features. The presented method achieved Area under Receiver Operating Curve (AUC) value equal to 0.92. Results indicate an interaction that affects lesion classification results of an area of an image used for feature extraction (lesion margin) and used texture features. The recommended set of features can be utilized in prostate cancer diagnosis and differentiation from benign prostate conditions.