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Published in: European Radiology 6/2018

01-06-2018 | Radiological Education

To share or not to share? Expected pros and cons of data sharing in radiological research

Authors: Francesco Sardanelli, Marco Alì, Myriam G. Hunink, Nehmat Houssami, Luca M. Sconfienza, Giovanni Di Leo

Published in: European Radiology | Issue 6/2018

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Abstract

The aims of this paper are to illustrate the trend towards data sharing, i.e. the regulated availability of the original patient-level data obtained during a study, and to discuss the expected advantages (pros) and disadvantages (cons) of data sharing in radiological research. Expected pros include the potential for verification of original results with alternative or supplementary analyses (including estimation of reproducibility), advancement of knowledge by providing new results by testing new hypotheses (not explored by the original authors) on pre-existing databases, larger scale analyses based on individual-patient data, enhanced multidisciplinary cooperation, reduced publication of false studies, improved clinical practice, and reduced cost and time for clinical research. Expected cons are outlined as the risk that the original authors could not exploit the entire potential of the data they obtained, possible failures in patients’ privacy protection, technical barriers such as the lack of standard formats, and possible data misinterpretation. Finally, open issues regarding data ownership, the role of individual patients, advocacy groups and funding institutions in decision making about sharing of data and images are discussed.

Key Points

Regulated availability of patient-level data of published clinical studies (data-sharing) is expected.
Expected benefits include verification/advancement of knowledge, reduced cost/time of research, clinical improvement.
Potential drawbacks include faults in patients’ identity protection and data misinterpretation.
Literature
1.
2.
go back to reference Boulton G, Rawlins M, Vallance P, Walport M (2011) Science as a public enterprise: the case for open data. Lancet 377:1633–1635CrossRefPubMed Boulton G, Rawlins M, Vallance P, Walport M (2011) Science as a public enterprise: the case for open data. Lancet 377:1633–1635CrossRefPubMed
3.
go back to reference Walport M, Brest P (2011) Sharing research data to improve public health. Lancet (London, England) 377:537–539CrossRef Walport M, Brest P (2011) Sharing research data to improve public health. Lancet (London, England) 377:537–539CrossRef
4.
go back to reference Taichman DB, Sahni P, Pinborg A et al (2017) Data sharing statements for clinical trials—A requirement of the International Committee of Medical Journal Editors. N Engl J Med 376:2277–2279CrossRefPubMed Taichman DB, Sahni P, Pinborg A et al (2017) Data sharing statements for clinical trials—A requirement of the International Committee of Medical Journal Editors. N Engl J Med 376:2277–2279CrossRefPubMed
5.
go back to reference Sconfienza LM, Sardanelli F (2013) Radiological journals in the online world: should we think open? Eur Radiol 23:1175–1177CrossRefPubMed Sconfienza LM, Sardanelli F (2013) Radiological journals in the online world: should we think open? Eur Radiol 23:1175–1177CrossRefPubMed
36.
go back to reference Collins FS, Tabak LA (2014) Policy: NIH plans to enhance reproducibility. Nature 505:612–613 Collins FS, Tabak LA (2014) Policy: NIH plans to enhance reproducibility. Nature 505:612–613
43.
go back to reference Krumholz HM, Waldstreicher J (2016) The Yale Open Data Access (YODA) project — A mechanism for data sharing. N Engl J Med 375:403–405 Krumholz HM, Waldstreicher J (2016) The Yale Open Data Access (YODA) project — A mechanism for data sharing. N Engl J Med 375:403–405
45.
go back to reference Academic Research Organization Consortium for Continuing Evaluation of Scientific Studies--Cardiovascular (ACCESS CV), Patel MR, Armstrong PW, Bhatt DL et al (2016) Sharing data from cardiovascular clinical trials—A Proposal. N Engl J Med 375:407–409CrossRef Academic Research Organization Consortium for Continuing Evaluation of Scientific Studies--Cardiovascular (ACCESS CV), Patel MR, Armstrong PW, Bhatt DL et al (2016) Sharing data from cardiovascular clinical trials—A Proposal. N Engl J Med 375:407–409CrossRef
46.
go back to reference Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMed Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMed
47.
go back to reference Herrick R, Horton W, Olsen T et al (2016) NeuroImage XNAT central: open sourcing imaging research data. NeuroImage 124:1093–1096CrossRefPubMed Herrick R, Horton W, Olsen T et al (2016) NeuroImage XNAT central: open sourcing imaging research data. NeuroImage 124:1093–1096CrossRefPubMed
49.
go back to reference Clark K, Vendt B, Smith K et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057CrossRefPubMedPubMedCentral Clark K, Vendt B, Smith K et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057CrossRefPubMedPubMedCentral
50.
go back to reference Armato SG, McLennan G, Bidaut L et al (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38:915–931CrossRefPubMedPubMedCentral Armato SG, McLennan G, Bidaut L et al (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38:915–931CrossRefPubMedPubMedCentral
51.
go back to reference Chen Y, Elenee Argentinis JD, Weber G (2016) IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin Ther 38:688–701CrossRefPubMed Chen Y, Elenee Argentinis JD, Weber G (2016) IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin Ther 38:688–701CrossRefPubMed
52.
go back to reference Loder E (2013) Sharing data from clinical trials: where we are and what lies ahead. BMJ 347:f4794–f4794CrossRefPubMed Loder E (2013) Sharing data from clinical trials: where we are and what lies ahead. BMJ 347:f4794–f4794CrossRefPubMed
53.
go back to reference Mello MM, Francer JK, Wilenzick M et al (2013) Preparing for responsible sharing of clinical trial data. N Engl J Med 369:1651–1658CrossRefPubMed Mello MM, Francer JK, Wilenzick M et al (2013) Preparing for responsible sharing of clinical trial data. N Engl J Med 369:1651–1658CrossRefPubMed
54.
go back to reference Anderson BJ, Merry AF (2009) Data sharing for pharmacokinetic studies. Paediatr Anaesth 19:1005–1010CrossRefPubMed Anderson BJ, Merry AF (2009) Data sharing for pharmacokinetic studies. Paediatr Anaesth 19:1005–1010CrossRefPubMed
56.
go back to reference Berlin JA, Morris S, Rockhold F et al (2014) Bumps and bridges on the road to responsible sharing of clinical trial data. Clin Trials 11:7–12CrossRefPubMed Berlin JA, Morris S, Rockhold F et al (2014) Bumps and bridges on the road to responsible sharing of clinical trial data. Clin Trials 11:7–12CrossRefPubMed
57.
go back to reference Peat G, Riley RD, Croft P et al (2014) Improving the transparency of prognosis research: the role of reporting, data sharing, registration, and protocols. PLoS Med 11:e1001671CrossRefPubMedPubMedCentral Peat G, Riley RD, Croft P et al (2014) Improving the transparency of prognosis research: the role of reporting, data sharing, registration, and protocols. PLoS Med 11:e1001671CrossRefPubMedPubMedCentral
59.
go back to reference Lee ES, McDonald DW, Anderson N, Tarczy-Hornoch P (2009) Incorporating collaboratory concepts into informatics in support of translational interdisciplinary biomedical research. Int J Med Inform 78:10–21CrossRefPubMed Lee ES, McDonald DW, Anderson N, Tarczy-Hornoch P (2009) Incorporating collaboratory concepts into informatics in support of translational interdisciplinary biomedical research. Int J Med Inform 78:10–21CrossRefPubMed
60.
64.
go back to reference Kasenda B, von Elm E, You J et al (2014) Prevalence, characteristics, and publication of discontinued randomized trials. JAMA 311:1045–1051CrossRefPubMed Kasenda B, von Elm E, You J et al (2014) Prevalence, characteristics, and publication of discontinued randomized trials. JAMA 311:1045–1051CrossRefPubMed
65.
go back to reference Clarke MJ, Stewart LA (1997) Meta-analyses using individual patient data. J Eval Clin Pract 3:207–212CrossRefPubMed Clarke MJ, Stewart LA (1997) Meta-analyses using individual patient data. J Eval Clin Pract 3:207–212CrossRefPubMed
66.
go back to reference Phi X-A, Houssami N, Obdeijn I-M et al (2015) Magnetic resonance imaging improves breast screening sensitivity in BRCA mutation carriers age ≥50 years: evidence from an individual patient data meta-analysis. J Clin Oncol 33:349–356CrossRefPubMed Phi X-A, Houssami N, Obdeijn I-M et al (2015) Magnetic resonance imaging improves breast screening sensitivity in BRCA mutation carriers age ≥50 years: evidence from an individual patient data meta-analysis. J Clin Oncol 33:349–356CrossRefPubMed
67.
go back to reference Marinovich ML, Macaskill P, Irwig L et al (2015) Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis. BMC Cancer 15:662CrossRefPubMedPubMedCentral Marinovich ML, Macaskill P, Irwig L et al (2015) Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis. BMC Cancer 15:662CrossRefPubMedPubMedCentral
69.
go back to reference Qi X, Deng H, Guo X (2017) Characteristics of retractions related to faked peer reviews: an overview. Postgrad Med J 93:499–503 Qi X, Deng H, Guo X (2017) Characteristics of retractions related to faked peer reviews: an overview. Postgrad Med J 93:499–503
71.
go back to reference Zarin DA (2013) Participant-level data and the new frontier in trial transparency. N Engl J Med 369:468–469CrossRefPubMed Zarin DA (2013) Participant-level data and the new frontier in trial transparency. N Engl J Med 369:468–469CrossRefPubMed
72.
go back to reference Farrar JT, Troxel AB, Haynes K et al (2014) Effect of variability in the 7-day baseline pain diary on the assay sensitivity of neuropathic pain randomized clinical trials: An ACTTION study. Pain 155:1622–1631CrossRefPubMed Farrar JT, Troxel AB, Haynes K et al (2014) Effect of variability in the 7-day baseline pain diary on the assay sensitivity of neuropathic pain randomized clinical trials: An ACTTION study. Pain 155:1622–1631CrossRefPubMed
73.
go back to reference Gabler NB, French B, Strom BL et al (2012) Validation of 6-minute walk distance as a surrogate end point in pulmonary arterial hypertension trials. Circulation 126:349–356CrossRefPubMedPubMedCentral Gabler NB, French B, Strom BL et al (2012) Validation of 6-minute walk distance as a surrogate end point in pulmonary arterial hypertension trials. Circulation 126:349–356CrossRefPubMedPubMedCentral
74.
go back to reference Gabler NB, French B, Strom BL et al (2012) Race and sex differences in response to endothelin receptor antagonists for pulmonary arterial hypertension. Chest 141:20–26CrossRefPubMed Gabler NB, French B, Strom BL et al (2012) Race and sex differences in response to endothelin receptor antagonists for pulmonary arterial hypertension. Chest 141:20–26CrossRefPubMed
75.
go back to reference Bierer BE, Crosas M, Pierce HH (2017) Data authorship as an incentive to data sharing. N Engl J Med 376:1684–1687CrossRefPubMed Bierer BE, Crosas M, Pierce HH (2017) Data authorship as an incentive to data sharing. N Engl J Med 376:1684–1687CrossRefPubMed
76.
go back to reference Prasser F, Bild R, Kuhn KA (2016) A generic method for assessing the quality of de-identified health data. Stud Health Technol Inform 228:312–316PubMed Prasser F, Bild R, Kuhn KA (2016) A generic method for assessing the quality of de-identified health data. Stud Health Technol Inform 228:312–316PubMed
77.
go back to reference Barocas S, Nissenbaum H (2014) Big data’s end run around anonymity and consent. In: Lane J, Stodden V, Bender S, Nissenbaum H (eds) Privacy, big data, and the public good. Cambridge University Press, New York, pp 44–75CrossRef Barocas S, Nissenbaum H (2014) Big data’s end run around anonymity and consent. In: Lane J, Stodden V, Bender S, Nissenbaum H (eds) Privacy, big data, and the public good. Cambridge University Press, New York, pp 44–75CrossRef
Metadata
Title
To share or not to share? Expected pros and cons of data sharing in radiological research
Authors
Francesco Sardanelli
Marco Alì
Myriam G. Hunink
Nehmat Houssami
Luca M. Sconfienza
Giovanni Di Leo
Publication date
01-06-2018
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 6/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-017-5165-5

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