Skip to main content
SpringerLink
Log in

Informatics in Radiation Oncology

  • Chapter
Machine Learning in Radiation Oncology

Abstract

Radiation oncology informatics includes informatics from the perspectives of every discipline involved in radiation oncology. As there are many open questions and an abundance of data, machine learning technologies can be valuable. Available data includes handwritten notes on paper, imaging data available in digital formats, radiation treatment plan details, financial data, and multilevel multicenter databases, to name a few. Tools of various complexity for various goals are available. The following chapter aims to portray this domain and present a selection of available tools.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17(6):1471–4.

    Article  PubMed  Google Scholar 

  2. Wright F, De Vito C, Langer B, Hunter A. Multidisciplinary cancer conferences: a systematic review and development of practice standards. Eur J Cancer. 2007;43(6):1002–10.

    Article  CAS  PubMed  Google Scholar 

  3. Cox JD, Stetz J, Pajak TF. Toxicity criteria of the radiation therapy oncology group (RTOG) and the European organization for research and treatment of cancer (EORTC). Int J Radiat Oncol Biol Phys. 1995;31(5):1341–6.

    Article  CAS  PubMed  Google Scholar 

  4. Aaronson NK, Ahmedzai S, Bergman B, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. 1993;85(5):365–76.

    Article  CAS  PubMed  Google Scholar 

  5. Prather JC, Lobach DF, Goodwin LK, Hales JW, Hage ML, Hammond WE. Medical data mining: knowledge discovery in a clinical data warehouse. In: Proceedings of the AMIA annual fall symposium. American Medical Informatics Association. Bethesda, Maryland, USA. 1997. p. 101.

    Google Scholar 

  6. Miller AA. Clinical information systems in oncology – making a difference to patient outcomes. Health Care Inf Rev Online. 2003

    Google Scholar 

  7. Röhner F, Schmucker M, Henne K, et al. Integration of the radiotherapy irradiation planning in the digital workflow. Strahlenther Onkol. 2013;189(2):111–6.

    Article  PubMed  Google Scholar 

  8. Jha AK, DesRoches CM, Campbell EG, et al. Use of electronic health records in US hospitals. N Engl J Med. 2009;360(16):1628–38.

    Article  CAS  PubMed  Google Scholar 

  9. Fong de los Santos L, Herman MG. Information flow through the radiation oncology process. In: Starkschall G, Siochi RAC, editors. Informatics in radiation oncology. Boca Raton: Taylor & Francis; 2013. p. 63–75.

    Google Scholar 

  10. Bleich HL, Slack WV. Designing a hospital information system: a comparison of interfaced and integrated systems. MD Comput. 1991;9(5):293–6.

    Google Scholar 

  11. Vorwerk H, Zink K, Wagner DM, Engenhart-Cabillic R. Making the right software choice for clinically used equipment in radiation oncology. Radiat Oncol. 2014;9(1):145.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Lenz R, Blaser R, Kuhn KA. Hospital information systems: chances and obstacles on the way to integration. Stud Health Technol Inform. 1999;68:25–30.

    CAS  PubMed  Google Scholar 

  13. Brooks KW, Fox TH, Davis LW. A critical look at currently available radiation oncology information management systems. Semin Radiat Oncol. 1997;7(1):49–57. Elsevier.

    Article  PubMed  Google Scholar 

  14. ASTRO. IHE-RO. 2014. https://www.astro.org/Practice-Management/IHE-RO/Index.aspx.

  15. Abdel-Wahab M, Rengan R, Curran B, Swerdloff S, Miettinen M, Field C, Ranjitkar S, Palta J, Tripuraneni P. Integrating the healthcare enterprise in radiation oncology plug and play–the future of radiation oncology? Int J Radiat Oncol Biol Phys. 2010;76(2):333–6.

    Google Scholar 

  16. Clifton C, Kantarcio M, Doan A, et al. Privacy-preserving data integration and sharing. In: Proceedings of the 9th ACM SIGMOD workshop on research issues in data mining and knowledge discovery. Paris: ACM; 2004. p. 19–26.

    Chapter  Google Scholar 

  17. Ratib O, Swiernik M, McCoy JM. From PACS to integrated EMR. Comput Med Imaging Graph. 2003;27(2):207–15.

    Article  PubMed  Google Scholar 

  18. Varian. ARIA – comprehensive oncology care. http://www.varian.com/euen/oncology/radiation_oncology/aria/.

  19. Elekta. Radiation oncology software – MOSAIQ® radiation oncology information system. http://www.elekta.com/healthcare-professionals/products/elekta-software/radiation-oncology.html.

  20. Ehrgott M, Holder A. Operations research methods for optimization in radiation oncology. J Radiat Oncol Inf. 2014;6(1):1–41.

    Google Scholar 

  21. Han Y, Huh SJ, Ju SG, et al. Impact of an electronic chart on the staff workload in a radiation oncology department. Jpn J Clin Oncol. 2005;35(8):470–4.

    Article  PubMed  Google Scholar 

  22. Bowman JS, Emerson SL, Darnovsky M. The practical SQL handbook: using structured query language. Reading: Addison-Wesley Longman Publishing Co., Inc.; 1996.

    Google Scholar 

  23. NEMA.org. Digital Imaging and Communications in Medicine (DICOM) – Supplement 11 – radiotherapy objects. 1997. ftp://medical.nema.org/medical/dicom/final/sup11_ft.pdf.

  24. Deasy JO, Apte AP. Open-source informatics tools for radiotherapy research. In: Starkschall G, Siochi RAC, editors. Informatics in radiation oncology. Boca Raton: Taylor & Francis; 2013. p. 147–60.

    Google Scholar 

  25. Thompson RF. RadOnc: an R package for analysis of dose-volume histogram and three-dimensional structural data. J Radiat Oncol Inf. 2014;6(1):98–110.

    Google Scholar 

  26. Panchal A, Keyes R. SU‐GG‐T‐260: dicompyler: an open source radiation therapy research platform with a plugin architecture. Med Phys. 2010;37(6):3245.

    Article  Google Scholar 

  27. Pieper S, Halle M, Kikinis R. 3D slicer. In: IEEE international symposium on Biomedical imaging: nano to macro 2004. IEEE, 2004. p. 632–5.

    Google Scholar 

  28. Pinter C, Lasso A, Wang A, Jaffray D, Fichtinger G. SlicerRT: radiation therapy research toolkit for 3D Slicer. Med Phys. 2012;39(10):6332–8.

    Article  PubMed  Google Scholar 

  29. Miller AA. A rational informatics-enabled approach to standardised nomenclature of contours and volumes in radiation oncology planning. J Radiat Oncol Inf. 2014;6(1):53–97.

    Google Scholar 

  30. Santanam L, Hurkmans C, Mutic S, et al. Standardizing naming conventions in radiation oncology. Int J f Radiat Oncol Biol Phys. 2012;83(4):1344–9.

    Article  Google Scholar 

  31. Deasy JO, Blanco AI, Clark VH. CERR: a computational environment for radiotherapy research. Med Phys. 2003;30(5):979–85.

    Article  PubMed  Google Scholar 

  32. Mathworks. Matlab documentation. http://es.mathworks.com/help/matlab/.

  33. Siochi RA, Pennington EC, Waldron TJ, Bayouth JE. Radiation therapy plan checks in a paperless clinic. J Appl Clin Med Phys: Am Coll Med Phys. 2009;10(1):2905.

    Article  Google Scholar 

  34. CERR. A computational environment for radiotherapy research. 2014. http://www.cerr.info/about.php.

  35. Graves T. RT_Image. 2014. http://rtimage.sourceforge.net/index.html.

  36. Yan Y, Dou Y, Weng X, Wallin A. SU‐GG‐T‐256: an enhanced DICOM‐RT viewer. Med Phys. 2010;37(6):3244.

    Google Scholar 

  37. Yan Y, Weng X, Penagaricano J, Ratanatharathorn V. A universal DICOM wizard to tackle incompatibility problems in the process of IMRT and IGRT. Int J Radiat Oncol Biol Phys. 2008;72(1):S657.

    Article  Google Scholar 

  38. Colonias A, Parda DS, Karlovits SM, et al. A radiation oncology based electronic health record in an integrated radiation oncology network. J Radiat Oncol Inf. 2011;3(1):3–11.

    Google Scholar 

  39. Fredrickson DH, Karzmark C, Rust DC, Tuschman M. Experience with computer monitoring, verification and record keeping in radiotherapy procedures using a Clinac-4. Int J Radiat Oncol Biol Phys. 1979;5(3):415–8.

    Article  CAS  PubMed  Google Scholar 

  40. SAP. SAP Crystal solutions: essential BI for small business. 2014. http://www.sap.com/solution/sme/software/analytics/crystal-bi/index.html.

  41. Sybase. Infomaker. 2014. http://infomaker.sharewarejunction.com/.

  42. Chong S, Anderson N, Finlay J. SU‐E‐T‐259: implementation of an automated workflow auditing and notification system for radiation oncology. Med Phys. 2011;38(6):3546.

    Article  Google Scholar 

  43. MacLennan J, Tang Z, Crivat B. Data mining with Microsoft SQL server 2008. Indianapolis: Wiley; 2011.

    Google Scholar 

  44. Plaisant C, Lam S, Shneiderman B, et al. Searching electronic health records for temporal patterns in patient histories: a case study with Microsoft Amalga. In: AMIA annual symposium proceedings; 2008. American Medical Informatics Association; 2008. p. 601.

    Google Scholar 

  45. Caradigm. http://www.caradigm.com. Accessed 3.3.2015.

  46. Morales J, Cho G. SU‐FF‐T‐213: evaluation of RadCalc V5. 2 as an independent monitor unit checking program for dynamic IMRT plans. Med Phys. Melville, New York, USA. 2009;36(6):2569.

    Google Scholar 

  47. Foong P, Looe H, Poppe B. SU‐E‐T‐544: commissioning and clinical evaluation of a secondary check software for 3D conformal and IMRT treatment plans. Med Phys. 2012;39(6):3830–1.

    Article  Google Scholar 

  48. Majithia L, DiCostanzo D, Weldon M, Gupta N, Rong Y. SU‐E‐T‐564: validation of photon dose calculation using Mobius3D system compared to AAA and Acuros XB systems. Med Phys. 2013;40(6):335.

    Article  Google Scholar 

  49. Bedford JL, Lee YK, Wai P, South CP, Warrington AP. Evaluation of the Delta4 phantom for IMRT and VMAT verification. Phys Med Biol. 2009;54(9):N167.

    Article  PubMed  Google Scholar 

  50. Li G, Zhang Y, Jiang X, et al. Evaluation of the ArcCHECK QA system for IMRT and VMAT verification. Phys Med. 2013;29(3):295–303.

    Article  PubMed  Google Scholar 

  51. Van Esch A, Huyskens DP, Behrens CF, et al. Implementing RapidArc into clinical routine: a comprehensive program from machine QA to TPS validation and patient QA. Med Phys. 2011;38(9):5146–66.

    Article  PubMed  Google Scholar 

  52. Torfeh T, Beaumont S, Guédon J-P, Bonnet D, Denis E, David L. Numerical 3D models used for an evaluation of software tools dedicated to an automatic quality control of EPID images. 2008.

    Google Scholar 

  53. Marinello G. Quality assurance for image-guided radiotherapy. 2008. http://www.iaea.org/inis/collection/NCLCollectionStore/_Public/40/003/40003891.pdf.

  54. Menon GV, Sloboda RS. Quality assurance measurements of a-Si EPID performance. Med Dosim. 2004;29(1):11–7.

    Article  PubMed  Google Scholar 

  55. Health Level Seven International. http://www.hl7.org/.

  56. Shaver D. The HL7 evolution-comparing HL7 versions 2 and 3. Corepoint Health. http://www.corepointhealth.com/sites/default/files/whitepapers/hl7-v2-v3-evolution pdf. Retrieved 2012; p. 16.

  57. NEMA.org. Members of the DICOM Standards Committee. http://medical.nema.org/members.pdf.

  58. Center for Advanced Brain Imaging. The DICOM standard. http://www.cabiatl.com/mricro/dicom/index.html.

  59. Piccirillo JF, Tierney RM, Costas I, Grove L, Spitznagel Jr EL. Prognostic importance of comorbidity in a hospital-based cancer registry. JAMA. 2004;291(20):2441–7.

    Article  CAS  PubMed  Google Scholar 

  60. Virnig BA, Warren JL, Cooper GS, Klabunde CN, Schussler N, Freeman J. Studying radiation therapy using SEER-Medicare-linked data. Med Care. 2002;40(8):IV-49–54.

    Google Scholar 

  61. Statistical Methodology and Applications Branch DMB, National Cancer Institute. Cansurv. 1.3 ed; 2014. http://surveillance.cancer.gov/cansurv/.

  62. Sullivan R, Peppercorn J, Sikora K, et al. Delivering affordable cancer care in high-income countries. Lancet Oncol. 2011;12(10):933–80.

    Article  PubMed  Google Scholar 

  63. Lambin P, Roelofs E, Reymen B, et al. Rapid Learning health care in oncology’ – an approach towards decision support systems enabling customised radiotherapy. Radiother Oncol. 2013;109(1):159–64.

    Article  PubMed  Google Scholar 

  64. EuroCat. about EuroCat. 2014. http://www.eurocat.info/information/about.html.

  65. McNutt T, Wong J, Purdy J, Valicenti R, DeWeese T. OncoSpace: A new paradigm for clinical research and decision support in radiation oncology. In: 10th international conference on computers in radiotherapy, Amsterdam; 2010.

    Google Scholar 

  66. Westberg J, Krogh S, Brink C, Vogelius I. A DICOM based radiotherapy plan database for research collaboration and reporting. J Phys: Conf Ser. 2014;489:012100. IOP Publishing.

    Google Scholar 

  67. Pyakuryal A, Myint WK, Gopalakrishnan M, Jang S, Logemann JA, Mittal BB. A computational tool for the efficient analysis of dose-volume histograms for radiation therapy treatment plans. J Appl Clinical Med Phys/Am Coll Med Phys. 2010;11(1):3013.

    Google Scholar 

  68. Putora PM, Blattner M, Papachristofilou A, Mariotti F, Paoli B, Plasswilm L. Dodes (diagnostic nodes) for guideline manipulation. J Radiat Oncol Inform. 2010;2(1):1–8.

    Google Scholar 

  69. Putora PM, Oldenburg J. Swarm-based medicine. J Med Internet Res. 2013;15(9), e207.

    Article  PubMed Central  PubMed  Google Scholar 

  70. Putora PM, Panje CM, Papachristofilou A, dal Pra A, Hundsberger T, Plasswilm L. Objective consensus from decision trees. Radiat Oncol. 2014;9(1):270.

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland

    Paul Martin Putora MD, PhD, MA & Samuel Peters

  2. Direktion ICT, Radiotherapy Applications, Zürich University Hospital, Zürich University, Zürich, Switzerland

    Marc Bovet PhD

Authors
  1. Paul Martin Putora MD, PhD, MA
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samuel Peters
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marc Bovet PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul Martin Putora MD, PhD, MA .

Editor information

Editors and Affiliations

  1. Department of Oncology, McGill University, Montreal, Canada

    Issam El Naqa

  2. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA

    Ruijiang Li

  3. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA

    Martin J. Murphy

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Putora, P.M., Peters, S., Bovet, M. (2015). Informatics in Radiation Oncology. In: El Naqa, I., Li, R., Murphy, M. (eds) Machine Learning in Radiation Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-18305-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18305-3_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18304-6

  • Online ISBN: 978-3-319-18305-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation