{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:42:41Z","timestamp":1788241361272,"version":"build-2803163510"},"reference-count":34,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T00:00:00Z","timestamp":1571961600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Radiomics is the process to automate tumor feature extraction from medical images. This has shown potential for quantifying the tumor phenotype and predicting treatment response. The three major challenges of radiomics research and clinical adoption are: (a) lack of standardized methodology for radiomics analyses, (b) lack of a universal lexicon to denote features that are semantically equivalent, and (c) lists of feature values alone do not sufficiently capture the details of feature extraction that might nonetheless strongly affect feature values (e.g. image normalization or interpolation parameters). These barriers hamper multicenter validation studies applying subtly different imaging protocols, preprocessing steps and radiomics software. We propose an open\u2010source ontology\u2010guided radiomics analysis workflow (O\u2010RAW) to address the above challenges in the following manner: (a) distributing a free and open\u2010source software package for radiomics analysis, (b) deploying a standard lexicon to uniquely describe features in common usage and (c) provide methods to publish radiomic features as a semantically interoperable data graph object complying to FAIR (findable accessible interoperable reusable) data principles.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>O\u2010RAW was developed in Python, and has three major modules using open\u2010source component libraries (PyRadiomics Extension and PyRadiomics). First, PyRadiomics Extension takes standard DICOM\u2010RT (Radiotherapy) input objects (i.e. a DICOM series and an RTSTRUCT file) and parses them as arrays of voxel intensities and a binary mask corresponding to a volume of interest (VOI). Next, these arrays are passed into PyRadiomics, which performs the feature extraction procedure and returns a Python dictionary object. Lastly, PyRadiomics Extension parses this dictionary as a W3C\u2010compliant Semantic Web \u201ctriple store\u201d (i.e., list of subject\u2010predicate\u2010object statements) with relevant semantic meta\u2010labels drawn from the radiation oncology ontology and radiomics ontology. The output can be published on an SPARQL endpoint, and can be remotely examined via SPARQL queries or to a comma separated file for further analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We showed that O\u2010RAW executed efficiently on four\u00a0datasets with different\u00a0modalities, RIDER (CT),\u00a0MMD (CT), CROSS (PET) and THUNDER (MR). The test was performed on an HP laptop running Windows 7 operating system and 8GB RAM on which we noted execution time including DICOM images and associated RTSTRUCT matching, binary mask conversion of a single VOI, batch\u2010processing of feature extraction (105 basic features in PyRadiomics), and the conversion to an resource description framework (RDF) object. The results were (RIDER) 407.3, (MMD) 123.5, (CROSS) 513.2 and (THUNDER) 128.9\u00a0s for a single VOI. In addition, we demonstrated a use case, taking images from a public repository and publishing the radiomics results as FAIR data in this study on <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.radiomics.org\">www.radiomics.org<\/jats:ext-link>. Finally, we provided a practical instance to show how a user could query radiomic features and track the calculation details based on the RDF graph object created by O\u2010RAW via a simple SPARQL query.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We implemented O\u2010RAW for FAIR radiomics analysis, and successfully published radiomic features from DICOM\u2010RT objects as semantic web triples. Its practicability and flexibility can greatly increase the development of radiomics research and ease transfer to clinical practice.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13844","type":"journal-article","created":{"date-parts":[[2019,10,3]],"date-time":"2019-10-03T14:54:33Z","timestamp":1570114473000},"page":"5677-5684","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Technical Note: Ontology\u2010guided radiomics analysis workflow (O\u2010RAW)"],"prefix":"10.1002","volume":"46","author":[{"given":"Zhenwei","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology (MAASTRO) GROW \u2013 School for Oncology and Development Biology Maastricht University Medical Centre+ Maastricht 6229 ET The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Traverso","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology 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