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Computational Radiomics System to Decode the Radiographic Phenotype

Institution:
1Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
2Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands.
3Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
4Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
HighWire
Publication Date:
Nov-2017
Journal:
Cancer Res
Volume Number:
77
Issue Number:
21
Pages:
e104-e107
Citation:
Cancer Res. 2017 Nov 1;77(21):e104-e107.
PubMed ID:
29092951
PMCID:
PMC5672828
Appears in Collections:
NAC, SLICER, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
U01 CA190234/CA/NCI NIH HHS/United States
U24 CA194354/CA/NCI NIH HHS/United States
Generated Citation:
van Griethuysen J.J.M., Fedorov A., Parmar C., Hosny A., Aucoin N., Narayan V., Beets-Tan R.G.H., Fillion-Robin J-C., Pieper S., Aerts H.J.W.L. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017 Nov 1;77(21):e104-e107. PMID: 29092951. PMCID: PMC5672828.
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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. Cancer Res; 77(21); e104-7. ©2017 AACR.