Computational Radiomics System to Decode the Radiographic Phenotype

Cancer Research - Tập 77 Số 21 - Trang e104-e107 - 2017
Joost J. M. van Griethuysen1,2,3, Andriy Fedorov4, Chintan Parmar1, Ahmed Hosny1, Nicole Aucoin4, Vivek Narayan1, Regina G. H. Beets‐Tan2,3, Jean‐Christophe Fillion‐Robin5, Steve Pieper6, Hugo J.W.L. Aerts1
11Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
22Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands.
33GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands,
44Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
55Kitware, Clifton Park, New York.
66Isomics, Cambridge, Massachusetts.

Tóm tắt

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 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.

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Cancer Research

Tập 77 Số 21

e104-e107

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