Author: Shahed Badiyan, Tsuicheng D. Chiu, Viktor M. Iakovenko, Steve Jiang, Christopher Kabat, Mu-Han Lin, Roberto Pellegrini, Arnold Pompos, Edoardo Salmeri, David Sher, Sruthi Sivabhaskar, Justin D. Visak 👨🔬
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, Global Clinical Science, Elekta AB, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center 🌍
Purpose: Adaptive treatment planning requires robust strategies to enable streamlined on-couch processes, creating a significant barrier for planners transitioning from conventional to adaptive planning. This often results in multiple planning iterations to identify the optimal strategy. Scripting capabilities were recently introduced in a MR-guided adaptive treatment planning system update that will support automated workflows. Herein, we leverage scripting and curated templates to streamline planning, reduce iterations, and improve efficiency for prostate and liver treatments.
Methods: Site-specific templates were developed in Monaco 6.2.2 based on institutional criteria for prostate and liver cases. Twenty previously treated patients: 10 prostate cases (40Gy with a 45Gy boost in 5 fractions) and 10 liver cases (40-50Gy in 3-5 fractions) were selected for template validation. Templates included optimization constraints, dosimetric criteria, beam arrangements, and sequencing parameters. A novel planning automation script was utilized to autonomously drive the pre-planning chart set-up and optimize each plan without human interference. Compliance with physician directives was evaluated after plan generation. Cases that required manual iterations after batch optimization were tracked.
Results: A total of 20 plans were evaluated. For prostate cases, 70% required no template modifications, while 30% needed 1-2 iterations to refine constraints for rectum V24Gy, bladder V18.3Gy, dose fall-off, or rectum V38Gy. Adjustments were more common in cases with minimal prostate-rectum separation. For liver cases, 60% required no modifications, and 40% needed 1-4 iterations to adjust constraints for duodenum, stomach, or dose fall-off. Larger target volumes (>120cc) or overlaps with critical structures required additional template adjustments.
Conclusion: Scripting and templates in Monaco 6.2.2 minimized plan iterations, with 70% of prostate and 60% of liver cases requiring no adjustments. The findings highlight the potential of automated workflows in bridging the gap between conventional and adaptive planning, paving the way for streamlined online adaptive radiotherapy processes and reduced planner burden.