Purpose : A software platform for online adaptive proton therapy (PT) based on open-source tools provides online decision tools to treat or re-plan. Automatic re-planning tools are under development. Methods: At treatment planning time, planning CT images (pCT), a treatment plan, dose maps and structure sets are sent to the DICOM PACS of the application. On the day of treatment, the receipt of the daily CT or CBCT on the PACS triggers an automatic computation of clinical indicators based on a prescribed treatment and the image of the day. The therapist accesses the result via a web-browser in the treatment room. The physicist and Physician can view the results remotely on any device, thus enabling remote decision-making including to allow the treatment as planned or to re-plan. [Too many words] Results: Daily Anatomical changes are accounted for either with the daily CT or with a virtual CT (vCT) computed by deformable registration of the pCT on the daily CBCT. The dose actually delivered by the plan is computed and used to determine the clinical indicators (WEPL and dose based). If a comparison between the daily indicators to the prescribed indicators exceeds a predetermined threshold, then a flag will be returned to notify the physicist or physician . This software is a platform for translational research for online adaptive PT bringing algorithms from the researcher’s office to the treatment room for clinical research studies. Conclusion: The application is of interest for clinical research in adaptive proton-therapy and it helps the clinical user to test and validate the workflows developed by the researchers in Proton-therapy. Additional workflows (for QA, re-planning, …) are under implementation with MCsquare as dose-engine. The open-source approach allows research groups to develop their own workflow to share with the community to speed up research in PT.
Arnaud Pin, Sophie Puydupin, L Yin, Souris, K., Janssens, G., & B Teo. (2017). A Platform for Adaptive Radiation-therapy Research. Medical Physics, 44(6), 2798. https://hdl.handle.net/2078.5/127200 (Original work published 2017)