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Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer

Objectives: Big data-based multicenter medical research is expected to bring significant advances to cancer treatment worldwide. However, there are concerns related to data sharing among multicenter networks. Clinical data can be shielded by firewalls using distributed research networks (DRNs). We a...

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Autores principales: Park, Jihwan, Lee, Ji Youl, Moon, Mi Hyoung, Park, Yong Hyun, Rho, Mi Jung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061631/
https://www.ncbi.nlm.nih.gov/pubmed/36977531
http://dx.doi.org/10.1177/15330338221149262
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author Park, Jihwan
Lee, Ji Youl
Moon, Mi Hyoung
Park, Yong Hyun
Rho, Mi Jung
author_facet Park, Jihwan
Lee, Ji Youl
Moon, Mi Hyoung
Park, Yong Hyun
Rho, Mi Jung
author_sort Park, Jihwan
collection PubMed
description Objectives: Big data-based multicenter medical research is expected to bring significant advances to cancer treatment worldwide. However, there are concerns related to data sharing among multicenter networks. Clinical data can be shielded by firewalls using distributed research networks (DRNs). We attempted to develop DRNs for multicenter research that can be easily installed and used by any institution. Patients and Methods: We propose a DRN for multicenter cancer research called the cancer research line (CAREL) and present a data catalog based on a common data model (CDM). CAREL was validated using 1723 patients with prostate cancer and 14 990 patients with lung cancer in a retrospective study. We used the attribute-value pairs and array data type JavaScript object notation (JSON) format to interface third-party security solutions such as blockchain. Results: We developed visualized data catalogs of prostate and lung cancer based on the observational medical outcomes partnership (OMOP) CDM, from which researchers can easily browse and select relevant data. We made the CAREL source code readily available for download and application for relevant purposes. In addition, it is possible to realize a multicenter research network using CAREL development sources. Conclusion: CAREL source can enable medical institutions to participate in multicenter cancer research. Our technology is open source, so small institutions that cannot afford to spend high costs can use it to develop a platform for multicenter research.
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spelling pubmed-100616312023-03-31 Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer Park, Jihwan Lee, Ji Youl Moon, Mi Hyoung Park, Yong Hyun Rho, Mi Jung Technol Cancer Res Treat Challenges in the application of machine learning in cancers Objectives: Big data-based multicenter medical research is expected to bring significant advances to cancer treatment worldwide. However, there are concerns related to data sharing among multicenter networks. Clinical data can be shielded by firewalls using distributed research networks (DRNs). We attempted to develop DRNs for multicenter research that can be easily installed and used by any institution. Patients and Methods: We propose a DRN for multicenter cancer research called the cancer research line (CAREL) and present a data catalog based on a common data model (CDM). CAREL was validated using 1723 patients with prostate cancer and 14 990 patients with lung cancer in a retrospective study. We used the attribute-value pairs and array data type JavaScript object notation (JSON) format to interface third-party security solutions such as blockchain. Results: We developed visualized data catalogs of prostate and lung cancer based on the observational medical outcomes partnership (OMOP) CDM, from which researchers can easily browse and select relevant data. We made the CAREL source code readily available for download and application for relevant purposes. In addition, it is possible to realize a multicenter research network using CAREL development sources. Conclusion: CAREL source can enable medical institutions to participate in multicenter cancer research. Our technology is open source, so small institutions that cannot afford to spend high costs can use it to develop a platform for multicenter research. SAGE Publications 2023-03-28 /pmc/articles/PMC10061631/ /pubmed/36977531 http://dx.doi.org/10.1177/15330338221149262 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Challenges in the application of machine learning in cancers
Park, Jihwan
Lee, Ji Youl
Moon, Mi Hyoung
Park, Yong Hyun
Rho, Mi Jung
Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer
title Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer
title_full Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer
title_fullStr Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer
title_full_unstemmed Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer
title_short Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer
title_sort cancer research line (carel): development of expanded distributed research networks for prostate cancer and lung cancer
topic Challenges in the application of machine learning in cancers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061631/
https://www.ncbi.nlm.nih.gov/pubmed/36977531
http://dx.doi.org/10.1177/15330338221149262
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