Segmentation methods and dosimetric evaluation of 3D-printed immobilization devices in head and neck radiotherapy.

Background: Treatment planning systems (TPS) often exclude immobilization devices from optimization and calculation, potentially leading to inaccurate dose estimates. This study employed deep learning methods to automatically segment 3D-printed head and neck immobilization devices and evaluate their dosimetric impact in head and neck VMAT.

Methods: Computed tomography (CT) positioning images from 49 patients were used to train the Mask2Former model to segment 3D-printed headrests and MFIFs. Based on the results, four body structure sets were generated for each patient to evaluate the impact on dose distribution in volumetric modulated arc therapy (VMAT) plans: S (without immobilization devices), S (with MFIFs), S (with 3D-printed headrests), and S (with both). VMAT plans (P, P, P, and P) were created for each structure set. Dose-volume histogram (DVH) data and dose distribution of the four plans were compared to assess the impact of the 3D-printed headrests and MFIFs on target and normal tissue doses. Gafchromic EBT3 film measurements were used for patient-specific verification to validate dose calculation accuracy.

Results: The Mask2Former model achieved a mean average precision (mAP) of 0.898 and 0.895, with a Dice index of 0.956 and 0.939 for the 3D-printed headrest on the validation and test sets, respectively. For the MFIF, the Dice index was 0.980 and 0.981 on the validation and test sets, respectively. Compared to P, P reduced the V for PGTVnx, PGTVnd, PGTVrpn, PTV1, and PTV2 by 5.99%, 6.51%, 5.93%, 2.24%, and 1.86%, respectively(P ≤ 0.004). P reduced the same targets by 1.78%, 2.56%, 1.75%, 1.16%, and 1.48%(P < 0.001), with a 31.3% increase in skin dose (P < 0.001). P reduced the V by 9.15%, 10.18%, 9.16%, 3.36%, and 3.28% (P < 0.001), respectively, while increasing the skin dose by 31.6% (P < 0.001). EBT3 film measurements showed that the P dose distribution was more aligned with actual measurements, achieving a mean gamma pass rate of 92.14% under the 3%/3 mm criteria.

Conclusions: This study highlights the potential of Mask2Former in 3D-printed headrest and MFIF segmentation automation, providing a novel approach to enhance personalized radiation therapy plan accuracy. The attenuation effects of 3D-printed headrests and MFIFs reduce V and D for PTVs in head and neck cancer patients, while the buildup effects of 3D-printed headrests increases the skin dose (31.3%). Challenges such as segmentation inaccuracies for small targets and artifacts from metal fasteners in MFIFs highlight the need for model optimization and validation on larger, more diverse datasets.