Beyond conventional bounds: Surpassing system limits for stereotactic ablative (SAbR) lung radiotherapy using CBCT-based adaptive planning system

J Appl Clin Med Phys. 2024 Aug;25(8):e14375. doi: 10.1002/acm2.14375. Epub 2024 May 7.

Abstract

Purpose: Online adaptive radiotherapy relies on a high degree of automation to enable rapid planning procedures. The Varian Ethos intelligent optimization engine (IOE) was originally designed for conventional treatments so it is crucial to provide clear guidance for lung SAbR plans. This study investigates using the Ethos IOE together with adaptive-specific optimization tuning structures we designed and templated within Ethos to mitigate inter-planner variability in meeting RTOG metrics for both online-adaptive and offline SAbR plans.

Methods: We developed a planning strategy to automate the generation of tuning structures and optimization. This was validated by retrospective analysis of 35 lung SAbR cases (total 105 fractions) treated on Ethos. The effectiveness of our planning strategy was evaluated by comparing plan quality with-and-without auto-generated tuning structures. Internal target volume (ITV) contour was compared between that drawn from CT simulation and from cone-beam CT (CBCT) at time of treatment to verify CBCT image quality and treatment effectiveness. Planning strategy robustness for lung SAbR was quantified by frequency of plans meeting reference plan RTOG constraints.

Results: Our planning strategy creates a gradient within the ITV with maximum dose in the core and improves intermediate dose conformality on average by 2%. ITV size showed no significant difference between those contoured from CT simulation and first fraction, and also trended towards decreasing over course of treatment. Compared to non-adaptive plans, adaptive plans better meet reference plan goals (37% vs. 100% PTV coverage compliance, for scheduled and adapted plans) while improving plan quality (improved GI (gradient index) by 3.8%, CI (conformity index) by 1.7%).

Conclusion: We developed a robust and readily shareable planning strategy for the treatment of adaptive lung SAbR on the Ethos system. We validated that automatic online plan re-optimization along with the formulated adaptive tuning structures can ensure consistent plan quality. With the proposed planning strategy, highly ablative treatments are feasible on Ethos.

Keywords: adaptive radiotherapy; lung SAbR; treatment‐planning.

MeSH terms

  • Algorithms
  • Cone-Beam Computed Tomography* / methods
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Lung Neoplasms* / diagnostic imaging
  • Lung Neoplasms* / radiotherapy
  • Lung Neoplasms* / surgery
  • Organs at Risk* / radiation effects
  • Radiosurgery* / methods
  • Radiotherapy Dosage*
  • Radiotherapy Planning, Computer-Assisted* / methods
  • Radiotherapy, Intensity-Modulated* / methods
  • Retrospective Studies