The potential use of big data in oncology

Willems, Stefan M.; Abeln, Sanne; Feenstra, K. Anton; Bree, Remco de; Poel, Egge F. van der; Jong, Robert J. Baatenburg de; Heringa, Jaap; Brekel, Michiel W. M. van den

doi:10.1016/j.oraloncology.2019.09.003

Cited by 47 publications

(48 citation statements)

References 20 publications

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“…The 32 publications were from January 2014 to October 2019: 3% (1/32) in 2014 [ 89 ]; 13% (4/32) in 2015 [ 82 , 95 , 96 , 108 ]; 19% (6/32) in 2016 [ 92 , 94 , 97 , 98 , 103 , 112 ]; 19% (6/32) in 2017 [ 83 , 99 , 101 , 102 , 104 , 106 ]; 19% (6/32) in 2018 [ 85 , 86 , 88 , 90 , 93 , 105 ]; 28% (9/32) in 2019 [ 81 , 84 , 87 , 91 , 100 , 107 , 109 - 111 ].…”

Section: Resultsmentioning

confidence: 99%

“…The majority of the publications (12/32, 38%) were from the United States [ 83 , 85 - 87 , 91 , 94 - 96 , 98 - 102 ]; 31% (10/32) were from the United Kingdom [ 82 , 88 , 89 , 92 , 97 , 103 , 105 , 107 , 108 ]. Four (12%) publications were from other European countries (2 from Switzerland [ 93 , 106 ], 1 from Slovenia [ 90 ], and 1 from the Netherlands [ 109 ]). Six (19%) publications were from other countries: Brazil [ 104 ], Canada [ 111 ], China [ 112 ], New Zealand [ 84 ], Singapore [ 110 ], and South Africa [ 81 ].…”

Section: Resultsmentioning

confidence: 99%

“…The publications dealing with the problem of ownership of patient information in the context of big data are published by the academic community in 27 journals covering different scientific areas: (1) medicine, biomedical sciences, and public health—15 journals, 47% (15/32) of publications [ 83 , 85 , 87 , 89 - 93 , 96 , 97 , 101 , 102 , 105 , 109 , 111 ]; (2) health and medical informatics—6 journals, 28% (9/32) of publications [ 82 , 86 , 88 , 98 , 100 , 104 , 107 , 108 , 112 ]; and (3) medical ethics, law, and health policy—6 journals, 25% (8/32) of publications [ 81 , 84 , 94 , 95 , 99 , 103 , 106 , 110 ].…”

Section: Resultsmentioning

confidence: 99%

“…The distribution of the articles according to the aspects of ownership of patient information in the context of big data as seen by the academic community is presented in Figure 2 . The following aspects were distinguished: (1) Ethical (16/32, 50%) [ 83 , 85 , 87 , 88 , 90 , 93 , 94 , 100 , 102 - 106 , 109 - 111 ]; (2) legal (11/32, 34%), including legal [ 81 , 82 , 92 , 96 , 101 , 112 ] and legal and ethical [ 84 , 95 , 99 , 107 , 108 ]; (3) political (3/32, 9%) , including political [ 89 , 97 ] and political and legal [ 86 ]; and (4) managerial (2/32, 6%) [ 91 , 98 ].…”

Section: Resultsmentioning

confidence: 99%

“…The distribution of the articles according to the proposed solutions for patient data ownership issues in the context of big data is presented in Figure 2 . The following types of solutions were proposed: (1) political (9/32, 28%), including political [ 97 , 98 , 100 , 106 , 108 ], political and legal [ 86 , 99 ], and political and technical [ 109 , 111 ]; (2) technical (7/32, 22%), including technical [ 82 , 83 , 88 , 90 , 101 , 112 ] and technical and legal [ 107 ]; (3) ethical and legal (6/32, 19%) [ 85 , 87 , 94 , 95 , 103 , 110 ]; and legal (5/32, 16%) [ 81 , 89 , 92 , 96 , 102 ].…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review

Mirchev¹,

Mircheva²,

Kerekovska³

2020

J Med Internet Res

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Background The ownership of patient information in the context of big data is a relatively new problem, which is not yet fully recognized by the medical academic community. The problem is interdisciplinary, incorporating legal, ethical, medical, and aspects of information and communication technologies, requiring a sophisticated analysis. However, no previous scoping review has mapped existing studies on the subject. Objective This study aims to map and assess published studies on patient data ownership in the context of big data as viewed by the academic community. Methods A scoping review was conducted based on the 5-stage framework outlined by Arksey and O’Malley and further developed by Levac, Colquhoun, and O’Brien. The organization and reporting of results of the scoping review were conducted according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses and its extensions for Scoping Reviews). A systematic and comprehensive search of 4 scientific information databases, PubMed, ScienceDirect, Scopus, and Springer, was performed for studies published between January 2000 and October 2019. Two authors independently assessed the eligibility of the studies and the extracted data. Results The review included 32 eligible articles authored by academicians that correspond to 3 focus areas: problem (ownership), area (health care), and context (big data). Five major aspects were studied: the scientific area of publications, aspects and academicians’ perception of ownership in the context of big data, proposed solutions, and practical applications for data ownership issues in the context of big data. The aspects in which publications consider ownership of medical data are not clearly distinguished but can be summarized as ethical, legal, political, and managerial. The ownership of patient data is perceived primarily as a challenge fundamental to conducting medical research, including data sales and sharing, and to a lesser degree as a means of control, problem, threat, and opportunity also in view of medical research. Although numerous solutions falling into 3 categories, technology, law, and policy, were proposed, only 3 real applications were discussed. Conclusions The issue of ownership of patient information in the context of big data is poorly researched; it is not addressed consistently and in its integrity, and there is no consensus on policy decisions and the necessary legal regulations. Future research should investigate the issue of ownership as a core research question and not as a minor fragment among other topics. More research is needed to increase the body of knowledge regarding the development of adequate policies and relevant legal frameworks in compliance with ethical standards. The combined efforts of multidisciplinary academic teams are needed to overcome existing gaps in the perception of ownership, the aspects of ownership, and the possible solutions to patient data ownership issues in the reality of big data.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review

Mirchev¹,

Mircheva²,

Kerekovska³

2020

J Med Internet Res

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Artificial intelligence in cancer research: learning at different levels of data granularity

2021

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From genome‐scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co‐existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

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From cancer big data to treatment: Artificial intelligence in cancer research

Danishuddin,

Khan,

Kim

2023

The Journal of Gene Medicine

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In recent years, developing the idea of “cancer big data” has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high‐dimensional multi‐omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.

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The potential use of big data in oncology

Cited by 47 publications

References 20 publications

The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review

The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review

Artificial intelligence in cancer research: learning at different levels of data granularity

From cancer big data to treatment: Artificial intelligence in cancer research

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