Mandar S Bhagwat, Ph.D.
Massachusetts General Hospital, Boston, USA

Automated checks during HDR BT procedures are needed to reduce human interventional errors.  

The report by AAPM’s Task Group 59¹ provides a blueprint for establishing a QA program in HDR BT. The Information Notice 2013-16 issued by the Nuclear Regulatory Commission (NRC) in 2013 emphasizes the need for verification of treatment parameters prior to HDR treatment². Incorrect manual entry into the treatment planning system (TPS) of the treatment distance has been found to be a major source of HDR treatment errors reported to the NRC and International Atomic Energy Agency (IAEA) between 1980 and 2001³. More recent analysis of HDR treatment errors reported to the NRC between 1999 and 2015 found that ~63% were due to either mislabeling of catheters, incorrect connection or wrong specification of treatment distance in the TPS, resulting in treatment targets being missed by 0.4 cm to 30 cm^3,4,5. This is so because, compared to external beam radiotherapy, HDR BT involves much more human intervention during treatment which increases probability for errors.

On the TPS end of therapy commercial secondary check QA software by different vendors is limited to calculating doses without checks for treatment distances, one of the main sources of errors if entered incorrectly. It has been shown that such TPS errors can be reduced in a timely manner by development of an independent plan verification software⁶. Most clinics do not have the resources to indigenously develop such programs tuned for their workflow.

TPS errors are carried over to the treatment console and can cause misadministration, if not caught before the treatment delivery. In addition, any errors from wrong labeling or incorrectly connecting catheters also need to be detected and rectified before treatment. Use of GaN probes⁷, pinhole systems⁸, MOSFET⁹, and scintillators¹⁰, are some of the methods that have been explored to spot incorrect catheter-transfer tube-afterloader connections during treatment by tracking the HDR source. These methods are resource-intensive, time-consuming or limited to phantom studies. This has prevented their wider applicability in most clinics.

The absence of automated real-time quality assurance HDR BT options requires greater vigilance on the part of physicists and therapists to prevent misadministration of treatment.

1. Kubo, H. Dale, Glenn P. Glasgow, Timothy D. Pethel, Bruce R. Thomadsen, and Jeffrey F. Williamson. “High dose‐rate brachytherapy treatment delivery: report of the AAPM Radiation Therapy Committee Task Group No. 59.” Medical physics25, no. 4 375-403 (1998).https://doi.org/10.1118/1.598232

2. US Nuclear Regulatory Commission. NRC Information Notice 2013-16: Importance of Verification of Treatment Parameters for High Dose-rate Remote Afterloader Administrations. (2013). https://www.nrc.gov/docs/ML1305/ML13058A306.pdf

3. Thomadsen, Bruce, Shi-Woei Lin, Patrick Laemmrich, Tonia Waller, Arif Cheng, Barrett Caldwell, Rebecca Rankin, and Judith Stitt. “Analysis of treatment delivery errors in brachytherapy using formal risk analysis techniques.” International Journal of Radiation Oncology* Biology* Physics57, no. 5, 1492-1508 (2003).https://doi.org/10.1016/s0360-3016(03)01622-5

4. Gao, W. Analysis of Medical Events in High-Dose-Rate Brachytherapy. Brachytherapy 12, S61 (2013). https://doi.org/10.1016/j.brachy.2013.01.122

5. Gao, W. Minimizing Targeting Error in High-Dose-Rate Brachytherapy with an End-to-End Source Positioning Test. Brachytherapy 15, S171–S172 (2016). https://doi.org/10.1016/j.brachy.2016.04.310

6. Damato, Antonio L., Phillip M. Devlin, Mandar S. Bhagwat, Ivan Buzurovic, Scott Friesen, Jorgen L. Hansen, Larissa J. Lee et al. “Independent brachytherapy plan verification software: improving efficacy and efficiency.” Radiotherapy and Oncology 113, no. 3, 420-424 (2014).https://doi.org/10.1016/j.radonc.2014.09.015

7. Wang, Ruoxi, Julien Ribouton, Patrick Pittet, Pierrick Guiral, P. Jalade, and Guo-Neng Lu. “Implementation of GaN based real-time source position monitoring in HDR brachytherapy.” Radiation measurements 71, 293-296 (2014).https://doi.org/10.1016/j.radmeas.2014.05.021

8. Alnaghy, Saree, Mitra Safavi-Naeini, Daniel R. Franklin, Zhangbo Han, Dean L. Cutajar, Marco Petasecca, Michael Lerch, and Anatoly B. Rosenfeld. “Analytical modelling and simulation of single and double cone pinholes for real-time in-body tracking of an HDR brachytherapy source.” IEEE Transactions on Nuclear Science 63, no. 3, 1375-1385 (2016). https://doi.org/10.1109/TNS.2016.2540635
9. Ivanova, Tatjana, Lynn Gilbert, and Dorin A. Todor. “High-dose-rate brachytherapy source tracking by MOSFET dosimetry system.” Brachytherapy 8, no. 2, 130 (2009). https://doi.org/10.1016/j.brachy.2009.03.065
10. Huynh, Elizabeth, Phillip Devlin, Ivan Buzurovic, Robert Cormack, Desmond O’Farrell, Thomas Harris, Jeremy Bredfeldt, and Mandar Bhagwat. “Real-Time Visual Tracking of the HDR Source during Skin Therapy Enabled by Scintillation Markers.” Brachytherapy 18, no. 3, S44 (2019).https://doi.org/10.1016/j.brachy.2019.04.092