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, R.; Klein, Stefan; Fijten, Rianne; Aerts, Hugo J.W.L.; Dekker, A.; Soest, Johan van; Wee, Leonard

doi:10.1002/mp.14805

Cited by 2 publications

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“…Some of the aforementioned issues can be ameliorated by following FAIR data principles. Kalendralis et al [14] have shown how semantic ontologies can be used to apply syntactic standards to, and provide descriptive metadata about feature extraction methods on top of, existing medical data but without editing the original source data itself. Semantic Web standards based on the resource descriptor framework (RDF) can be used to store data with persistent unique identifiers, in such a way that is completely agnostic to the underlying database schema in the original data source [15].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Big Imaging Data Pipeline Adhering to FAIR Principles for Federated Machine Learning in Oncology

Jha

Mithun

Sherkhane

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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Cancer is a fatal disease and one of the leading causes of death worldwide. The cure rate in cancer treatment remains low; hence, cancer treatment is gradually shifting toward personalized treatment. Artificial intelligence (AI) and radiomics have been recognized as one of the potential areas of research in personalized medicine in oncology. Several researchers have identified the capabilities of AI and radiomics to characterize phenotype and there by predict the outcome of treatment in oncology. Although AI and radiomics have shown promising initial results in diagnosis and treatment in oncology, these technologies are also facing challenges of standardization and scalability. In the last few years, researchers have been trying to develop a research infrastructure for federated machine learning that increases the usability of Big Data for clinical research. These research infrastructures are based on the findable, accessible, interoperable, and reusable (i.e., FAIR) data principles. The India-Dutch "big imaging data approach for oncology in a Netherlands India collaboration" (BIONIC) is a jointly funded initiative by the Dutch Research Council (NWO) and the Indian Ministry of Electronics and Information Technology (MeitY), aiming to introduce radiomic-based research into clinical environments using federated machine learning on geographically dispersed collections of FAIR data. This article described a prototype end-to-end research infrastructure implemented through the BIONIC partnership into a leading cancer care public hospital in India.

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Section: Introductionmentioning

confidence: 99%

“…Above and beyond the FAIR principles, scientific collaboration can be encouraged at the health systems and institutions level by homogenizing data collection in clinical routine procedures and allowing multi-institutional sharing of data [14].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Big Imaging Data Pipeline Adhering to FAIR Principles for Federated Machine Learning in Oncology

Jha

Mithun

Sherkhane

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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“…In “FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, Interobserver, Lung1 and Head‐Neck1 TCIA collections” by Kalendralis et al 10 describe updated clinical data, radiomics features, and Digital Imaging and Communications in Medicine (DICOM) headers from four datasets analyzed as part of their Nature Communications radiomics study 11 in order to support repeatability, reproducibility, generalizability, and transparency in radiomics research, which can be used as useful benchmark for future CT radiomics studies.…”

Section: Introductionmentioning

confidence: 99%

Introduction to special issue on datasets hosted in The Cancer Imaging Archive (TCIA)

et al. 2020

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

Abstract: Published in Medical Physics, Vol. 47, No. 11

Cited by 2 publications

References 0 publications

Implementation of Big Imaging Data Pipeline Adhering to FAIR Principles for Federated Machine Learning in Oncology

Implementation of Big Imaging Data Pipeline Adhering to FAIR Principles for Federated Machine Learning in Oncology

Introduction to special issue on datasets hosted in The Cancer Imaging Archive (TCIA)

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