Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region

Patrik Sibolt; Lina M Andersson; Lucie Calmels; David Sjöström; Ulf Bjelkengren; Poul Geertsen; Claus F Behrens

doi:10.1016/j.phro.2020.12.004

Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region

Phys Imaging Radiat Oncol. 2020 Dec 18:17:1-7. doi: 10.1016/j.phro.2020.12.004. eCollection 2021 Jan.

Authors

Patrik Sibolt¹, Lina M Andersson¹, Lucie Calmels¹, David Sjöström¹, Ulf Bjelkengren¹, Poul Geertsen¹, Claus F Behrens¹

Affiliation

¹ Department of Oncology, Herlev & Gentofte Hospital, Herlev, Denmark.

Abstract

Background and purpose: Studies have demonstrated the potential of online adaptive radiotherapy (oART). However, routine use has been limited due to resource demanding solutions. This study reports on experiences with oART in the pelvic region using a novel cone-beam computed tomography (CBCT)-based, artificial intelligence (AI)-driven solution.

Material and methods: Automated pre-treatment planning for thirty-nine pelvic cases (bladder, rectum, anal, and prostate), and one hundred oART simulations were conducted in a pre-clinical release of Ethos (Varian Medical Systems, Palo Alto, CA). Plan quality, AI-segmentation accuracy, oART feasibility and an integrated calculation-based quality assurance solution were evaluated. Experiences from the first five clinical oART patients (three bladder, one rectum and one sarcoma) are reported.

Results: Auto-generated pre-treatment plans demonstrated similar planning target volume (PTV) coverage and organs at risk doses, compared to institution reference. More than 75% of AI-segmentations during simulated oART required none or minor editing and the adapted plan was superior in 88% of cases. Limitations in AI-segmentation correlated to cases where AI model training was lacking. The five first treated patients complied well with the median adaptive procedure duration of 17.6 min (from CBCT acceptance to treatment delivery start). The treated bladder patients demonstrated a 42% median primary PTV reduction, indicating a 24%-30% reduction in V_45Gy to the bowel cavity, compared to non-ART.

Conclusions: A novel commercial oART solution was demonstrated feasible for various pelvic sites. Clinically acceptable AI-segmentation and auto-planning enabled adaptation within reasonable timeslots. Possibilities for reduced PTVs observed for bladder cancer indicated potential for toxicity reductions.

Keywords: Artificial Intelligence; Automated treatment planning; Bladder cancer; CBCT image-guided radiotherapy; Online Adaptive Radiotherapy (oART); Workflow.