Computational Radiomics System to Decode the Radiographic Phenotype

Citation:

Joost JM van Griethuysen, Andriy Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, Vivek Narayan, Regina GH Beets-Tan, Jean-Christophe Fillion-Robin, Steve Pieper, and Hugo JWL Aerts. 2017. “Computational Radiomics System to Decode the Radiographic Phenotype.” Cancer Res, 77, 21, Pp. e104-e107.

Abstract:

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 noninvasive 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 , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io 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. .
Last updated on 04/18/2019