MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection


This paper presents a quantitative radiomics feature model for performing prostate cancer detection using multiparametric MRI (mpMRI). It incorporates a novel tumor candidate identification algorithm to efficiently and thoroughly identify the regions of concern and constructs a comprehensive radiomics feature model to detect tumorous regions. In contrast to conventional automated classification schemes, this radiomics-based feature model aims to ground its decisions in a way that can be interpreted and understood by the diagnostician. This is done by grouping features into high-level feature categories which are already used by radiologists to diagnose prostate cancer: Morphology, Asymmetry, Physiology, and Size (MAPS), using biomarkers inspired by the PI-RADS guidelines for performing structured reporting on prostate MRI. Clinical mpMRI data were collected from 13 men with histology-confirmed prostate cancer and labeled by an experienced radiologist. These annotated data were used to train classifiers using the proposed radiomics-driven feature model in order to evaluate the classification performance. The preliminary experimental results indicated that the proposed model outperformed each of its constituent feature groups as well as a comparable conventional mpMRI feature model. A further validation of the proposed algorithm will be conducted using a larger dataset as future work.

DOI: 10.1109/TBME.2015.2485779
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@article{Cameron2016MAPSAQ, title={MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection}, author={Andrew Cameron and Farzad Khalvati and Masoom A. Haider and Alexander Wong}, journal={IEEE transactions on bio-medical engineering}, year={2016}, volume={63 6}, pages={1145-56} }