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FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections

PURPOSE: One of the most frequently cited radiomics investigations showed that features automatically extracted from routine clinical images could be used in prognostic modeling. These images have been made publicly accessible via The Cancer Imaging Archive (TCIA). There have been numerous requests...

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Autores principales: Kalendralis, Petros, Shi, Zhenwei, Traverso, Alberto, Choudhury, Ananya, Sloep, Matthijs, Zhovannik, Ivan, Starmans, Martijn P.A., Grittner, Detlef, Feltens, Peter, Monshouwer, Rene, Klein, Stefan, Fijten, Rianne, Aerts, Hugo, Dekker, Andre, van Soest, Johan, Wee, Leonard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754296/
https://www.ncbi.nlm.nih.gov/pubmed/32521049
http://dx.doi.org/10.1002/mp.14322
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author Kalendralis, Petros
Shi, Zhenwei
Traverso, Alberto
Choudhury, Ananya
Sloep, Matthijs
Zhovannik, Ivan
Starmans, Martijn P.A.
Grittner, Detlef
Feltens, Peter
Monshouwer, Rene
Klein, Stefan
Fijten, Rianne
Aerts, Hugo
Dekker, Andre
van Soest, Johan
Wee, Leonard
author_facet Kalendralis, Petros
Shi, Zhenwei
Traverso, Alberto
Choudhury, Ananya
Sloep, Matthijs
Zhovannik, Ivan
Starmans, Martijn P.A.
Grittner, Detlef
Feltens, Peter
Monshouwer, Rene
Klein, Stefan
Fijten, Rianne
Aerts, Hugo
Dekker, Andre
van Soest, Johan
Wee, Leonard
author_sort Kalendralis, Petros
collection PubMed
description PURPOSE: One of the most frequently cited radiomics investigations showed that features automatically extracted from routine clinical images could be used in prognostic modeling. These images have been made publicly accessible via The Cancer Imaging Archive (TCIA). There have been numerous requests for additional explanatory metadata on the following datasets — RIDER, Interobserver, Lung1, and Head–Neck1. To support repeatability, reproducibility, generalizability, and transparency in radiomics research, we publish the subjects’ clinical data, extracted radiomics features, and digital imaging and communications in medicine (DICOM) headers of these four datasets with descriptive metadata, in order to be more compliant with findable, accessible, interoperable, and reusable (FAIR) data management principles. ACQUISITION AND VALIDATION METHODS: Overall survival time intervals were updated using a national citizens registry after internal ethics board approval. Spatial offsets of the primary gross tumor volume (GTV) regions of interest (ROIs) associated with the Lung1 CT series were improved on the TCIA. GTV radiomics features were extracted using the open‐source Ontology‐Guided Radiomics Analysis Workflow (O‐RAW). We reshaped the output of O‐RAW to map features and extraction settings to the latest version of Radiomics Ontology, so as to be consistent with the Image Biomarker Standardization Initiative (IBSI). Digital imaging and communications in medicine metadata was extracted using a research version of Semantic DICOM (SOHARD, GmbH, Fuerth; Germany). Subjects’ clinical data were described with metadata using the Radiation Oncology Ontology. All of the above were published in Resource Descriptor Format (RDF), that is, triples. Example SPARQL queries are shared with the reader to use on the online triples archive, which are intended to illustrate how to exploit this data submission. DATA FORMAT: The accumulated RDF data are publicly accessible through a SPARQL endpoint where the triples are archived. The endpoint is remotely queried through a graph database web application at http://sparql.cancerdata.org. SPARQL queries are intrinsically federated, such that we can efficiently cross‐reference clinical, DICOM, and radiomics data within a single query, while being agnostic to the original data format and coding system. The federated queries work in the same way even if the RDF data were partitioned across multiple servers and dispersed physical locations. POTENTIAL APPLICATIONS: The public availability of these data resources is intended to support radiomics features replication, repeatability, and reproducibility studies by the academic community. The example SPARQL queries may be freely used and modified by readers depending on their research question. Data interoperability and reusability are supported by referencing existing public ontologies. The RDF data are readily findable and accessible through the aforementioned link. Scripts used to create the RDF are made available at a code repository linked to this submission: https://gitlab.com/UM‐CDS/FAIR‐compliant_clinical_radiomics_and_DICOM_metadata.
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spelling pubmed-77542962020-12-23 FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections Kalendralis, Petros Shi, Zhenwei Traverso, Alberto Choudhury, Ananya Sloep, Matthijs Zhovannik, Ivan Starmans, Martijn P.A. Grittner, Detlef Feltens, Peter Monshouwer, Rene Klein, Stefan Fijten, Rianne Aerts, Hugo Dekker, Andre van Soest, Johan Wee, Leonard Med Phys Medical Physics Dataset Articles PURPOSE: One of the most frequently cited radiomics investigations showed that features automatically extracted from routine clinical images could be used in prognostic modeling. These images have been made publicly accessible via The Cancer Imaging Archive (TCIA). There have been numerous requests for additional explanatory metadata on the following datasets — RIDER, Interobserver, Lung1, and Head–Neck1. To support repeatability, reproducibility, generalizability, and transparency in radiomics research, we publish the subjects’ clinical data, extracted radiomics features, and digital imaging and communications in medicine (DICOM) headers of these four datasets with descriptive metadata, in order to be more compliant with findable, accessible, interoperable, and reusable (FAIR) data management principles. ACQUISITION AND VALIDATION METHODS: Overall survival time intervals were updated using a national citizens registry after internal ethics board approval. Spatial offsets of the primary gross tumor volume (GTV) regions of interest (ROIs) associated with the Lung1 CT series were improved on the TCIA. GTV radiomics features were extracted using the open‐source Ontology‐Guided Radiomics Analysis Workflow (O‐RAW). We reshaped the output of O‐RAW to map features and extraction settings to the latest version of Radiomics Ontology, so as to be consistent with the Image Biomarker Standardization Initiative (IBSI). Digital imaging and communications in medicine metadata was extracted using a research version of Semantic DICOM (SOHARD, GmbH, Fuerth; Germany). Subjects’ clinical data were described with metadata using the Radiation Oncology Ontology. All of the above were published in Resource Descriptor Format (RDF), that is, triples. Example SPARQL queries are shared with the reader to use on the online triples archive, which are intended to illustrate how to exploit this data submission. DATA FORMAT: The accumulated RDF data are publicly accessible through a SPARQL endpoint where the triples are archived. The endpoint is remotely queried through a graph database web application at http://sparql.cancerdata.org. SPARQL queries are intrinsically federated, such that we can efficiently cross‐reference clinical, DICOM, and radiomics data within a single query, while being agnostic to the original data format and coding system. The federated queries work in the same way even if the RDF data were partitioned across multiple servers and dispersed physical locations. POTENTIAL APPLICATIONS: The public availability of these data resources is intended to support radiomics features replication, repeatability, and reproducibility studies by the academic community. The example SPARQL queries may be freely used and modified by readers depending on their research question. Data interoperability and reusability are supported by referencing existing public ontologies. The RDF data are readily findable and accessible through the aforementioned link. Scripts used to create the RDF are made available at a code repository linked to this submission: https://gitlab.com/UM‐CDS/FAIR‐compliant_clinical_radiomics_and_DICOM_metadata. John Wiley and Sons Inc. 2020-06-27 2020-11 /pmc/articles/PMC7754296/ /pubmed/32521049 http://dx.doi.org/10.1002/mp.14322 Text en © 2020 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Medical Physics Dataset Articles
Kalendralis, Petros
Shi, Zhenwei
Traverso, Alberto
Choudhury, Ananya
Sloep, Matthijs
Zhovannik, Ivan
Starmans, Martijn P.A.
Grittner, Detlef
Feltens, Peter
Monshouwer, Rene
Klein, Stefan
Fijten, Rianne
Aerts, Hugo
Dekker, Andre
van Soest, Johan
Wee, Leonard
FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections
title FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections
title_full FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections
title_fullStr FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections
title_full_unstemmed FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections
title_short FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections
title_sort fair‐compliant clinical, radiomics and dicom metadata of rider, interobserver, lung1 and head‐neck1 tcia collections
topic Medical Physics Dataset Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754296/
https://www.ncbi.nlm.nih.gov/pubmed/32521049
http://dx.doi.org/10.1002/mp.14322
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