Repeatability of Multiparametric Prostate MRI Radiomics Features

Year: 2019

Type: article

Abstract: In this study we assessed the repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.

Source: Scientific Reports

DOI: 10.1038/s41598-019-45766-z

Authors: Michael Schwier, Joost van Griethuysen, Mark G. Vangel, Steve Pieper, Sharon Peled, Clare Tempany, Hugo J. W. L. Aerts, Ron Kikinis, Fiona M. Fennessy, Andriy Fedorov

Institutions: Brigham and Women's Hospital, Harvard University, The Netherlands Cancer Institute, Maastricht University, Massachusetts General Hospital, Dana-Farber Cancer Institute, University of Bremen, Fraunhofer Institute for Digital Medicine

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,790

Funders: U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering

OpenAlex ID: W2883648998