Computational Radiomics System to Decode the Radiographic Phenotype

Abstract

<p class="first" id="P1">Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed <i>PyRadiomics</i>, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. <i>PyRadiomics</i> is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of <i>PyRadiomics</i> and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at <a data-untrusted="" href="http://www.radiomics.io" id="d1301279e211" target="xrefwindow">www.radiomics.io</a>. With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. </p>