TY - JOUR
T1 - Computational Radiomics System to Decode the Radiographic Phenotype
AU - van Griethuysen, Joost J. M.
AU - Fedorov, Andriy
AU - Parmar, Chintan
AU - Hosny, Ahmed
AU - Aucoin, Nicole
AU - Narayan, Vivek
AU - Beets-Tan, Regina G. H.
AU - Fillion-Robin, Jean-Christophe
AU - Pieper, Steve
AU - Aerts, Hugo J. W. L.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - 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 PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics 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. (C) 2017 AACR.
AB - 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 PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics 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. (C) 2017 AACR.
KW - TEXTURE ANALYSIS
KW - F-18-FDG PET
KW - HETEROGENEITY
U2 - 10.1158/0008-5472.CAN-17-0339
DO - 10.1158/0008-5472.CAN-17-0339
M3 - Article
C2 - 29092951
SN - 0008-5472
VL - 77
SP - E104-E107
JO - Cancer Research
JF - Cancer Research
IS - 21
ER -