Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework

The use of high-volume quantitative radiomics features extracted from multi-parametric magnetic resonance imaging (MP-MRI) is gaining attraction for the autodetection of prostate tumors, since it provides a plethora of mineable data, which can be used for both detection and prognosis of prostate cancer. While current voxel-resolution radiomics-driven prostate tumor detection approaches utilize quantitative radiomics features associated with individual voxels on an independent basis, the incorporation of additional information regarding the spatial and radiomics feature relationships between voxels has significant potential for achieving a more reliable detection performance. Motivated by this, we present a novel approach for automatic prostate cancer detection using a radiomics-driven conditional random field (RD-CRF) framework. In addition to the high-throughput extraction and utilization of a comprehensive set of voxel-level quantitative radiomics features, the proposed RD-CRF framework leverages inter-voxel spatial and radiomics feature relationships to ensure that the autodetected tumor candidates exhibit interconnected tissue characteristics reflective of cancerous tumors. We evaluated the performance of the proposed framework using clinical prostate MP-MRI data of 20 patients, and the results of RD-CRF framework demonstrated a clear improvement with respect to the state-of-the-art in quantitative radiomics for automatic voxel-resolution prostate cancer detection.