Decision Support for Adapt-on-Demand Adaptive Radiotherapy for Head and Neck Cancer
Abstract
Purpose
To quantitatively benchmark dosimetric variation associated with tumor regression during head and neck (HN) radiotherapy and to evaluate the benefit of adaptive replanning as a basis for adapt-on-demand decision support.
Methods
Five HN patients were used to simulate tumor volume change throughout a course of treatment. For each patient, four additional CTs were synthesized to simulate different stages of tumor, with maximum variation of 20 mm. The synthesized CT with the largest tumor volume was designated as the planning CT, on which target volumes were contoured and a treatment plan was optimized. Three other synthesized CTs and the original CT representing progressive tumor regression were designated to simulate anatomical changes. For each regression level, the reference non-adaptive plan (non-ADP plan) was recalculated based on anatomy-of-the-day, and an adapted plan (ADP plan) was generated by re-optimizing. All plans were generated in the Eclipse treatment planning system (v18.2). Key dosimetric endpoints, including parotids, oral cavity, larynx, pharynx D50%, brainstem and cord Dmax, PTV V100% and plan homogeneity index (HI), were assessed for both non-ADP and ADP plans.
Results
Non-ADP plans resulted in slight degradation of plan quality, characterized by an increased mean D50% to ipsilateral parotid by 0.3 Gy. ADP plans significantly improved OAR sparing, reducing ipsilateral parotid D50% by 8.4 Gy and oral cavity D50% by 6.7 Gy for the final stage of tumor volume. ADP plans showed better overall PTV HI of 6.1%, 5.7%, 5.7%, 5.0% across all stages while non-ADP HI were 5.6%, 6.4%, 7.0.%, 7.2%.
Conclusion
Tumor regression can substantially compromise overall non-adaptive HN radiotherapy plan quality. Adaptive replanning effectively improves OAR sparing while maintaining target coverage. These findings support the feasibility of anatomy-informed decision support to identify the temporal checkpoint most likely to benefit from adaptive radiotherapy.