Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images

• Published in: Scientific reports Vol. 9; no. 1; p. 1570
• Main Authors: Varghese, Bino, Chen, Frank, Hwang, Darryl, Palmer, Suzanne L, De Castro Abreu, Andre Luis, Ukimura, Osamu, Aron, Monish, Aron, Manju, Gill, Inderbir, Duddalwar, Vinay, Pandey, Gaurav
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07-02-2019; Nature Publishing Group
• Subjects: 59/57; 631/114/1305; 692/308/575; 692/4025/1752; Classification; Humanities and Social Sciences; Learning algorithms; Magnetic resonance imaging; multidisciplinary; Prostate cancer; Radiomics; Risk assessment; Science; Science (multidisciplinary); Statistical analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-38381-x

Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods. This paper presents a systematic and rigorous framework comprised of classification, cross-validation and statistical analyses that was developed to identify the best performing classifier for PCa risk stratification based on mpMRI-derived radiomic features derived from a sizeable cohort. This classifier performed well in an independent validation set, including performing better than PI-RADS v2 in some aspects, indicating the value of objectively interpreting mpMRI images using radiomics and classification methods for PCa risk assessment.