Computational Radiomics System to Decode the Radiographic Phenotype

By Joost J. M. van Griethuysen, Andrey Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, V. Narayan, R. Beets-Tan, J. Fillion-Robin, S. Pieper, Hugo J. W. L. Aerts. Cancer Research, 2017

5,426 citations recorded by the source corpus.

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 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.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c53fa445bee435f567828db3a0ede0f08abdd0d5

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning (2019, methodology): “The radiomics model extracted 1108 features by the PyRadiomics toolkit [37] and selected eight features using recursive feature elimination (RFE).”
Radiomics with artificial intelligence: a practical guide for beginners. (2019, neutral): “are MaZda (32), LIFEx (33), PyRadiomics (34),”
Data Analysis Strategies in Medical Imaging (2018, background): “This process, referred to as “radiomics”(3–6), uses either a set of predefined engineered features(7) that describe radiographic aspects of shape, intensity, and texture, or alternatively features that can be automatically "deep learned" directly from example images(8,9).”
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge (2017, methodology): “We compute shape features (13 features), first order statistics (19 features) and gray level co-occurence matrix features (28 features) with the pyradiomics package [16].”
Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries (2018, background): “In other words, the hand-deigned features are not stable [11], which highly reduces the model reliability and applicability.”
Predicting cancer outcomes with radiomics and artificial intelligence in radiology (2021, background): “On the basis of the type of clinical decisions they can inform on, biomarkers can be grouped into several categories 12 .”
Pneumonia Detection On Chest X-Ray Using Radiomic Features And Contrastive Learning (2021, methodology): “For xv , we apply the PyRadiomics2 to extract 102-dimensional quantitative features, and [21] showed the details of these quantitative features and extraction process.”