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Using data extracted from a radiotherapy record & verify system to infer outcomes of patients treated for radical brain cancer

Published online by Cambridge University Press:  24 May 2019

Martin Sykes Open the ORCID record for Martin Sykes [Opens in a new window]
Martin Sykes*
Affiliation:
Radiotherapy, Hull University Teaching Hospitals NHS Trust, Castle Road, Cottingham, East Riding of Yorkshire, England
*
Address for correspondence: Martin Sykes, Radiotherapy, Queens Centre, Castle Hill Hospital, Cottingham, HU16 5JQ, UK. Tel: 01482 461216. E-mail: [email protected]
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Abstract

Aim:

The aims of this study were to explore the outcome measures that can be recorded in a radiotherapy IT system and the extract mortality results for a group of patients receiving radical radiotherapy treatment for primary brain cancer.

Method:

Treatment mortality outcomes were extracted from a radiotherapy database and were compared to treatment technique used between 1 January 2011 and 31 December 2017. The patients selected received 1 course of radiotherapy of 60 Gray in 30 treatments (n = 270). These patients received either Conformal Radiotherapy (CRT) (n = 127) or Volumetric Modulated Arc Therapy (VMAT) (n = 143). Kaplan–Meier plots were generated for these two groups to assess the survival. The median survival was 20·1 months (95%CI = 16·8−23·4) and 14·0 months (95%CI = 11·1−16·5) for CRT and VMAT, respectively.

Discussion:

Surprisingly, the results of this data extraction demonstrated that CRT gave better survival for this group of patients, than VMAT. The reason for the difference in survival is unclear and more data are needed to explain the result.

Conclusion:

This demonstrates that not only that a radiotherapy database can be used to extract outcome measures but that it must be done to explore where a change in treatment delivery has been of benefit to the patients or not.

Keywords

Glioblastomainformaticsoutcomesradiotherapysurvival
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 19 , Issue 1 , March 2020 , pp. 4 - 9
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Copyright
© Cambridge University Press 2019 

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