First experiences with an adaptive pelvic radiotherapy system: Analysis of treatment times and learning curve.
Authors
Affiliations (2)
Affiliations (2)
- Radiation Oncology, hôpital de La Tour, Meyrin, Switzerland.
- Radiation Oncology, hôpital de La Tour, Meyrin, Switzerland; Department of Radiation Oncology, centre hospitalier de l'université de Montréal, Montréal, Québec, Canada. Electronic address: [email protected].
Abstract
The Varian Ethos system allows not only on-treatment-table plan adaptation but also automated contouring with the aid of artificial intelligence. This study evaluates the initial clinical implementation of an adaptive pelvic radiotherapy system, focusing on the treatment times and the associated learning curve. We analyzed the data from 903 consecutive treatments for most urogenital cancers at our center. The treatment time was calculated from the time of the first cone-beam computed tomography scan used for replanning until the end of treatment. To calculate whether treatments were generally shorter over time, we divided the date of the first treatment into 3-months quartiles. Differences between the groups were calculated using t-tests. The mean time from the first cone-beam computed tomography scan to the end of treatment was 25.9min (standard deviation: 6.9min). Treatment time depended on the number of planning target volumes and treatment of the pelvic lymph nodes. The mean time from cone-beam computed tomography to the end of treatment was 37 % longer if the pelvic lymph nodes were treated and 26 % longer if there were more than two planning target volumes. There was a learning curve: in linear regression analysis, both quartiles of months of treatment (odds ratio [OR]: 1.3, 95 % confidence interval [CI]: 1.8-0.70, P<0.001) and the number of planning target volumes (OR: 3.0, 95 % CI: 2.6-3.4, P<0.001) were predictive of treatment time. Approximately two-thirds of the treatments were delivered within 33min. Treatment time was strongly dependent on the number of separate planning target volumes. There was a continuous learning curve.