MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection

• Published in: IEEE transactions on biomedical engineering Vol. 63; no. 6; pp. 1145 - 1156
• Main Authors: Cameron, Andrew, Khalvati, Farzad, Haider, Masoom A., Wong, Alexander
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automation; Biological system modeling; Cancer; Categories; Classification; Computer- Aided Detection; Decisions; Feature extraction; Feature Models; Histology; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Morphology; Multi-Parametric MRI; Physiology; Prostate; Prostate - diagnostic imaging; Prostate cancer; Prostate Cancer Detection; Prostate MRI; Prostatic Neoplasms - diagnostic imaging; Radiomics; Tumors; feature models; radiomics; multi-parametric MRI (mpMRI); prostate MRI; Computer-aided detection; prostate cancer detection
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2015.2485779

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.