Management of the radiotherapy quality control using automated Big Data processing

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Abstract

Introduction. In Moscow, the state-of-the-art information technologies for cancer care data processing are widely used in routine practice. Data Science approaches are increasingly applied in the field of radiation oncology. Novel arrays of radiotherapy performance indices can be introduced into real-time cancer care quality and safety monitoring.

The purpose of the study. The short review of the critical structural elements of automated Big Data processing and its perspectives in the light of the internal quality and safety control organization in radiation oncology departments.

Material and methods. The PubMed (Medline) and E-Library databases were used to search the articles published mainly in the last 2-3 years. In total, about 20 reports were selected.

Results. This paper highlights the applicability of the next-generation Data Science approaches to quality and safety assurance in radiation oncological units. The structural pillars for automated Big Data processing are considered. Big Data processing technologies can facilitate improvements in quality management at any radiotherapy stage. Simultaneously, the high requirements for quality and integrity across indices in the databases are crucial. Detailed dose data may also be linked to outcomes and survival indices integrated into larger registries.

Discussion. Radiotherapy quality control could be automated to some extent through further introduction of information technologies making comparisons of the real-time quality measures with digital targets in terms of minimum norms / standards. The implementation of automated systems generating early electronic notifications and rapid alerts in case of serious quality violation could drastically improve the internal medical processes in local clinics.

Conclusion. The role of Big Data tools in internal quality and safety control will dramatically increase over time.

About the authors

Aleksandr A. Zavyalov

Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department

Author for correspondence.
Email: noemail@neicon.ru
ORCID iD: 0000-0003-1825-1871
Russian Federation

Dmitry A. Andreev

Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department

Email: dmitry.email08@gmail.com
ORCID iD: 0000-0003-0745-9474

М.D., Ph.D., Leading Research Fellow, Scientific - Clinical Department, the State Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department, Moscow, 115088, Russia.

e-mail: dmitry.email08@gmail.com

Russian Federation

References

  1. Minakov S.N., Levina Yu.V., Prostov M.Yu. Population-based cancer register. functionality, challenges, and existing problems. Zlokachestvennye opukholi. 2019; 9(1): 6-9. https://doi.org/10.18027/2224-5057-2019-9-1-6-9 (in Russian)
  2. Pogonin A.V., Tyazhel’nikov A.A., Yumukyan A.V. United Medical Information and Analytical System of Moscow (UMIAS) is a tool for effective management of medical institutions. Zdravookhranenie. 2015; (4): 52–7. (in Russian)
  3. Meyer P., Noblet V., Mazzara C., Lallement A. Survey on deep learning for radiotherapy. Comput. Biol. Med. 2018; 98: 126–46. https://doi.org/10.1016/j.compbiomed.2018.05.018
  4. Matuszak M.M., Fuller C.D., Yock T.I., Hess C.B., McNutt T., Jolly S., et al. Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology. Med. Phys. 2018; 45(10): e811-e9. https://doi.org/10.1002/mp.13136
  5. Andreev D.A., Khachanova N.V., Stepanova V.N., Bashlakova E.E., Evdoshenko E.P., Davydovskaya M.V. Standardized modeling of the chronic disease progression (review). Problemy standartizatsii v zdravookhranenii. 2017; (9-10): 12–24. https://doi.org/10.26347/1607-2502201709-10012-024 (in Russian)
  6. Chiesa S., Tolu B., Longo S., Nardiello B., Capocchiano N.D., Rea F., et al. A new standardized data collection system for brain stereotactic external radiotherapy: the PRE.M.I.S.E project. Future Sci. O.A. 2020; 6(7): FSO596. https://doi.org/10.2144/fsoa-2020-0015
  7. Andreev D.A., Khachanova N.V., Kokushkin K.A., Davydovskaya M.V. Multiple sclerosis registries as a vital element in the transition to the value-based healthcare. Problemy standartizatsii v zdravookhranenii. 2018; (3-4): 35–45. https://doi.org/10.26347/1607-2502201803-04035-045 (in Russian)
  8. McNutt T.R., Bowers M., Cheng Z., Han P., Hui X., Moore J., et al. Practical data collection and extraction for big data applications in radiotherapy. Med. Phys. 2018; 45(10): e863-e9. https://doi.org/10.1002/mp.12817
  9. Hauth F., Bizu V., App R., Lautenbacher H., Tenev A., Bitzer M., et al. Electronic Patient-Reported Outcome Measures in Radiation Oncology: Initial Experience After Workflow Implementation. JMIR mHealth uHealth. 2019; 7(7): e12345. https://doi.org/10.2196/12345
  10. Lewis G.D., Hatch S.S., Wiederhold L.R., Swanson T.A. Long-Term Institutional Experience With Telemedicine Services for Radiation Oncology: A Potential Model for Long-Term Utilization. Adv. Radiat. Oncol. 2020; 5(4): 780–2. https://doi.org/10.1016/j.adro.2020.04.018
  11. Zhang B., Chen S., D’Souza W.D., Yi B. A systematic quality assurance framework for the upgrade of radiation oncology information systems. Phys. Med. 2020; 69: 28–35. https://doi.org/10.1016/j.ejmp.2019.11.024
  12. Clunie D., Hosseinzadeh D., Wintell M., De Mena D., Lajara N., Garcia-Rojo M., et al. Digital imaging and communications in medicine whole slide imaging connectathon at Digital Pathology Association Pathology Visions 2017. J. Pathol. Inform. 2018; 9: 6. https://doi.org/10.4103/jpi.jpi_1_18
  13. Teng D., Kong J., Wang F. Scalable and flexible management of medical image big data. Distrib. Parallel Databases. 2019; 37(2): 235–50. https://doi.org/10.1007/s10619-018-7230-8
  14. Nikiema J.N., Jouhet V., Mougin F. Integrating cancer diagnosis terminologies based on logical definitions of SNOMED CT concepts. J. Biomed. Inform. 2017; 74: 46–58. https://doi.org/10.1016/j.jbi.2017.08.013
  15. McNutt T.R., Moore K.L., Wu B., Wright J.L. Use of Big Data for Quality Assurance in Radiation Therapy. Semin. Radiat. Oncol. 2019; 29(4): 326–32. https://doi.org/10.1016/j.semradonc.2019.05.006
  16. Kiselev K.V., Noeva E.A., Vyborov O.N., Zorin A.V., Potekhina A.V., Osyaeva M.K., et al. Development of a reasoning solver algorithm for instrumental diagnostics of angina pectoris in intelligent clinical decision support system. Meditsinskie tekhnologii. Otsenka i vybor. 2019; 1(35): 32–42. https://doi.org/10.31556/2219-0678.2019.35.1.032-042 (in Russian)
  17. Metel’skaya A.V., Kamynina N.N. Development of the concept of «lean polyclinics». Problemy sotsial’noy gigieny, zdravookhraneniya i istorii meditsiny. 2020; 28(S): 785–90. https://doi.org/10.32687/0869-866X-2020-28-s1-785-790 (in Russian)
  18. Cai B., Altman M.B., Reynoso F., Garcia-Ramirez J., He A., Edward S.S., et al. Standardization and automation of quality assurance for high-dose-rate brachytherapy planning with application programming interface. Brachytherapy. 2019; 18(1): 108-114.e1. https://doi.org/10.1016/j.brachy.2018.09.004
  19. Stervik L., Pettersson N., Scherman J., Behrens C.F., Ceberg C., Engelholm S., et al. Analysis of early respiratory-related mortality after radiation therapy of non-small-cell lung cancer: feasibility of automatic data extraction for dose-response studies. Acta Oncol. 2020; 59(6): 628–35. https://doi.org/10.1080/0284186X.2020.1739331
  20. Vogelius I.R., Petersen J., Bentzen S.M. Harnessing data science to advance radiation oncology. Mol. Oncol. 2020; 14(7): 1514–28. https://doi.org/10.1002/1878-0261.12685
  21. Ronneberger O., Fischer P., Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N., Hornegger J., Wells W., Frangi A., eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Cham: Springer International Publishing; 2015: 234–41. https://doi.org/10.1007/978-3-319-24574-4_28

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