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

Jayane Julia Pereira da Silva Caio Weber Mendanha Ribeiro J. Ludwig Daniela Rocha Medrado Bruno Nunes Melo da Silva Ayane Cristine Alves Sarmento Edilmar de Moura Santos
Jul 2026 · Journal of Applied Clinical Medical Physics · Vol 27 · 0 citations · 18 references
Medicine

TL;DR

The proposed methodology demonstrated high sensitivity for detecting dosimetric deviations, with potential to enhance radiotherapy safety and effectiveness and guide objective replanning strategies.

Abstract

Abstract 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.

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