Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies

Abstract

Radiomics utilizes high-dimensional imaging data to discover the association with diagnostic, prognostic, predictive endpoint or radiogenomics. It is an emerging field of study that potentially depicts the intratumoral heterogeneity from quantitative and classified high-throughput data. The radiomics approach has an analytic pipeline where the imaging features are extracted, processed and analyzed. At this point, special data handling is essential because it faces issues of a high-dimensional biomarker compared to a single biomarker approach. This article describes the potential role of radiomics in oncologic studies, the basic analytic pipeline and special data handling with high-dimensional data to facilitate the radiomics approach as a tool for personalized medicine in oncology.

Keywords: High-dimensional; Imaging; Magnetic resonance; Modeling; Neuro-oncology; Radiomics.

Conflict of interest statement

Compliance with Ethical StandardsJi Eun Park and Ho Sung Kim declare that they have no conflict of interest.All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.The institutional review board of our institute approved this retrospective study, and the requirement to obtain informed consent was waived.

Figures

Fig. 1

The radiomics pipeline. Part I includes image acquisition, registration and segmentation. Signal intensity normalization is conducted for conventional MR imaging with signal intensity of arbitrary units. Part II includes feature extraction. Part III includes modeling according to the outcomes, with special consideration for high-dimensional data

Fig. 2

The heatmap demonstrates that diagnostic accuracy can be changed depending on which feature-selection method and classification method is applied. Color scale: expressed from yellow (accuracy, 100%) to blue (accuracy, 65%)

Fig. 3

The heatmap is a representation for radiomics analysis, with each row expressing one z-score normalized feature, while each column represents a single patient. GBM = glioblastoma, PCNSL = primary central nervous system lymphoma