Abstract
We propose a de-identification system which runs in a standalone mode. The system takes care of the de-identification of radiation oncology patient’s clinical and annotated imaging data including RTSTRUCT, RTPLAN, and RTDOSE. The clinical data consists of diagnosis, stages, outcome, and treatment information of the patient. The imaging data could be the diagnostic, therapy planning, and verification images. Archival of the longitudinal radiation oncology verification images like cone beam CT scans along with the initial imaging and clinical data are preserved in the process. During the de-identification, the system keeps the reference of original data identity in encrypted form. These could be useful for the re-identification if necessary.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Gillies R.J., Kinahan P.E., Hricak H.: Radiomics: images are more than pictures, they are data. Radiology 278 (2): 563–577, 2016
Prior F., Smith K., Sharma A., Kirby J., Tarbox L., Clark K., Bennett W., Nolan T., Freymann J.: The public cancer radiology imaging collections of The Cancer Imaging Archive. Scient Data 4: 170124, 2017
Robinson J.D.: Beyond the DICOM header: additional issues in deidentification. Am. J. Roentgenol. 203 (6): W658–W664, 2014
Kohli M.D., Summers R.M., Geis J.R.: Medical image data and datasets in the era of machine learning-whitepaper from the 2016 C-MIMI meeting dataset session. J. Digit. Imaging 30 (4): 392–399, 2017
Landau Y., Kiryati N. (2019) Dataset growth in medical image analysis research. arXiv:1908.07765
Vcelak P., Kryl M., Kratochvil M., Kleckova J.: Identification and classification of DICOM files with burned-in text content. Int. J. Med. Inform. 126: 128–137, 2019
Silva J.M., Pinho E., Monteiro E., Silva J.F., Costa C.: Controlled searching in reversibly de-identified medical imaging archives. J. Biomed. Inform. 77: 81–90, 2018
Genereaux B.W., Dennison D.K., Ho K., Horn R., Silver E.L., O’Donnell K., Kahn C.E.: DICOM web: Background and application of the web standard for medical imaging. J. Digit. Imaging 31 (3): 321–326, 2018
PS6.2: DICOM Standard, http://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html
Noumeir R.: DICOM structured report document type definition. IEEE Trans. Inform. Technol. Biomed. 7 (4): 318–328, 2003
Abouakil D., Heurix J., Neubauer T.: 2011 44th Hawaii international conference on system sciences.. In: 2011 44th Hawaii International Conference on System Sciences, IEEE, pp 1–11, 2011
Newhauser W., Jones T., Swerdloff S., Newhauser W., Cilia M., Carver R., Halloran A., Zhang R.: Anonymization of DICOM electronic medical records for radiation therapy. Comput. Bio. Med. 53: 134–140, 2014
Gorthi S., Bach C.M., Thiran J.P.: Exporting contours to DICOM-RT structure set. Insight J. 1: 1–18, 2009
Law M.Y., Liu B.: DICOM-RT and its utilization in radiation therapy. Radiographics 29 (3): 655–667, 2009
PS3.3: DICOM Standard, http://dicom.nema.org/medical/dicom/2017d/output/chtml/part03/sect_C.8.8.html
Aryanto K.Y.E., Oudkerk M., Van O.: PMA Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy. Eur. Radiol. 25 (12): 3685–3695, 2015
Freymann J.B., Kirby J.S., Perry J.H., Clunie D.A., Jaffe C.C.: Image data sharing for biomedical research—meeting HIPAA requirements for de-identification. J. Digit.Imaging 25 (1): 14–24, 2012
Warnock M.J., Toland C., Evans D., Wallace B., Nagy P.: Benefits of using the DCM 4 CHE DICOM archive. J. Digit. Imaging 20 (1): 125–129, 2007
Kan M.W.K., Leung L.H.T., Peter K.N.: The use of biologically related model (Eclipse) for the intensity-modulated radiation therapy planning of nasopharyngeal carcinomas. PloS One 9 (11): e112229, 2014
Pieper S., Halle M., Kikinis R.: 3D Slicer.. In: 2004 2nd IEEE international symposium on biomedical imaging nano to macro (IEEE Cat No. 04 EX 821),IEEE, pp 632–635, 2004
Muschelli J.: Recommendations for processing head CT data. Front. Neuroinform. 13: 61, 2019
Aryanto K.Y.E., Oudkerk M, Van O.: PMA Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy. Eur. Radiol. 25 (12): 3685–3695, 2015. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636522/table/Tab4/?report=objectonly
Acknowledgments
The work is carried out under National Digital Library of India (NDLI) sponsored by Ministry of Human Resource Development (MHRD), Govt. of India (approval no. IIT/SRIC/CS/NDM/2018-19/096).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
This study was funded by the Ministry of Human Resource Development IN (IIT/SRIC/CS/NDM/2018-19/096). None of the authors have potential conflicts of interest. The CHAVI protocol is approved by the institutional review board at the Tata Medical Center Kolkata. The reference no is EC/GOVT/24/IRB23 on 31st August 2018. All pattient who’s images have been biobank have given written informed consent.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection: Transactional Processing Systems
Appendix
Appendix
Algorithm
Study date = s, Random date = r, Difference between original date and random date = d, Original Date = date, Treatment reference date = TRD;
d = s-r or d= date-r; TRD day = TotalDay + d; TRD = ConvertToDate(TRD day);
Rights and permissions
About this article
Cite this article
Kundu, S., Chakraborty, S., Chatterjee, S. et al. De-Identification of Radiomics Data Retaining Longitudinal Temporal Information. J Med Syst 44, 99 (2020). https://doi.org/10.1007/s10916-020-01563-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-020-01563-0