Dosimetric correlation between exit dose maps and anatomical changes in head and neck cancer treatment on Halcyon‐E: A gamma analysis approach

BACKGROUND: Head and neck cancer (HNC) has a high incidence in Brazil. Although Image-Guided Radiation Therapy (IGRT) improves treatment precision, anatomical changes during the treatment course may compromise dose distribution and require replanning. PURPOSE: This study investigated the correlation between exit dose fluence and anatomical volumetric variations during HNC radiotherapy. Subsequently, a decision support methodology was proposed and validated to flag potential dosimetric deviations and guide objective replanning strategies. METHODS: A retrospective study was conducted with 11 patients, totaling 312 fractions treated on a Halcyon-E linear accelerator. Volumetric variations were derived from cone-beam computed tomography (CBCT) and exit dose fluence maps from each fraction were compared with the reference from the first treatment day using gamma analysis (γ_(1%/1 mm) to γ_(5%/5 mm); 10% threshold). Correlations between normalized volumetric variation (ΔV) and gamma indices were assessed using Kendall's tau coefficient (α = 0.05). A decision support methodology for replanning (γ_(1%/1 mm) < 80% and γ_(2%/2 mm) < 90%) was proposed and retrospectively validated in an independent cohort of 20 patients, comprising 618 treatment fractions. RESULTS: Strong negative correlations were observed between ΔV and gamma indices, with γ_(1%/1 mm) showing the highest sensitivity (τ = -0.83), followed by γ_(2%/2 mm) (τ = -0.75). Volumetric variations exceeding 5% were associated with significant dosimetric degradation. The validation correctly identified all replanned patients and detected potential indicators of dosimetric divergence not recognized in routine clinical practice. CONCLUSIONS: The thresholds γ_(1%/1 mm) < 80% and γ_(2%/2 mm) < 90%, combined with ΔV > 5%, proved effective for guiding replanning decisions. The proposed methodology demonstrated high sensitivity for detecting dosimetric deviations, with potential to enhance radiotherapy safety and effectiveness.