Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view

Delaby, N.; Barateau, A.; Chiavassa, S.; Biston, Marie-Claude; Chartier, Philippe; Graulières, E.; Guinement, L.; Huger, S.; Lacornerie, T.; Millardet-Martin, Corinne; Sottiaux, Alain; Caron, J.; Gensanne, D.; Pointreau, Y.; Biau, J.; Serre, Anne-Agathe; Castelli, J.; Tomsej, M.; García, R.; Khamphan, C.; Badey, A.

doi:10.1016/j.ejmp.2023.102568

Cited by 6 publications

(5 citation statements)

References 159 publications

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“…Meanwhile, there was no significant difference in EUD for the spinal canal and the plexus brachialis, probably because the primary focus of IGRT was laid upon reproducible positioning of the vertebrae. There exist different modalities of 3-dimensional imaging, which allow adaptive radiation therapy by calculating the spatial dose, kV-CBCT, MV-CBCT or MR-guided [ 25 ], each of which has its limitations and challenges. An alternative to CBCT-based ART is MR-guided online adaptive radiotherapy (MRgART).…”

Section: Discussionmentioning

confidence: 99%

“…NCT04883281, NCT01874587, NCT04901234, NCT04172753, NCT03972072, https://www.clinicaltrials.gov/ ]. Preceding, retrospective dosimetric studies report a beneficial dose distribution of ART compared to classical IGRT [ 14 – 17 , 23 , 25 , 43 ]. A single prospective, randomized phase 3-trial published so far on weekly, offline plan adaptation showed a rather sober picture without a clear clinical benefit of offline ART compared to standard IGRT for the primary endpoint, sparing the parotid glands [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…A more close-to-patient approach is online adaptive radiation therapy. It allows the instant online planning within one session [ 25 ]. Contrary to retrospective studies on ART or offline ART, this allows to react instantly to CTV deformations onboard during online navigation.…”

Section: Introductionmentioning

confidence: 99%

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Prospects for online adaptive radiation therapy (ART) for head and neck cancer

Guberina,

Hoffmann

et al. 2024

Radiat Oncol

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Background The aim of the present study is to examine the impact of kV-CBCT-based online adaptive radiation therapy (ART) on dosimetric parameters in comparison to image-guided-radiotherapy (IGRT) in consecutive patients with tumors in the head and neck region from a prospective registry. Methods The study comprises all consecutive patients with tumors in the head and neck area who were treated with kV-CBCT-based online ART or IGRT-modus at the linear-accelerator ETHOS™. As a measure of effectiveness, the equivalent-uniform-dose was calculated for the CTV (EUDCTV) and organs-at-risk (EUDOAR) and normalized to the prescribed dose. As an important determinant for the need of ART the interfractional shifts of anatomic landmarks related to the tongue were analyzed and compared to the intrafractional shifts. The latter determine the performance of the adapted dose distribution on the verification CBCT2 postadaptation. Results Altogether 59 consecutive patients with tumors in the head-and-neck-area were treated from 01.12.2021 to 31.01.2023. Ten of all 59 patients (10/59; 16.9%) received at least one phase within a treatment course with ART. Of 46 fractions in the adaptive mode, irradiation was conducted in 65.2% of fractions with the adaptive-plan, the scheduled-plan in the remaining. The dispersion of the distributions of EUDCTV-values from the 46 dose fractions differed significantly between the scheduled and adaptive plans (Ansari-Bradley-Test, p = 0.0158). Thus, the 2.5th percentile of the EUDCTV-values by the adaptive plans amounted 97.1% (95% CI 96.6–99.5%) and by the scheduled plans 78.1% (95% CI 61.8–88.7%). While the EUDCTV for the accumulated dose distributions stayed above 95% at PTV-margins of ≥ 3 mm for all 8 analyzed treatment phases the scheduled plans did for margins ≥ 5 mm. The intrafractional anatomic shifts of all 8 measured anatomic landmarks were smaller than the interfractional with overall median values of 8.5 mm and 5.5 mm (p < 0.0001 for five and p < 0.05 for all parameters, pairwise comparisons, signed-rank-test). The EUDOAR-values for the larynx and the parotid gland were significantly lower for the adaptive compared with the scheduled plans (Wilcoxon-test, p < 0.001). Conclusions The mobile tongue and tongue base showed considerable interfractional variations. While PTV-margins of 5 mm were sufficient for IGRT, ART showed the potential of decreasing PTV-margins and spare dose to the organs-at-risk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Prospects for online adaptive radiation therapy (ART) for head and neck cancer

Guberina,

Hoffmann

et al. 2024

Radiat Oncol

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“…Thus, utilizing kV-CBCT images for patient position alignment within the treatment room can effectively address these limitations. However, the direct application of kV-CBCT images for re-planning is constrained due to various factors, including inconsistent CT numbers, image quality issues related to scattered artifacts and noise, as well as limitations in the field-of-view [7][8][9]. Therefore, prior to utilizing kV-CBCT in ART, it is necessary to perform kV-CBCT correction.…”

Section: Introductionmentioning

confidence: 99%

“…In the existing literature, several techniques have been proposed for performing kV-CBCT dose calculation [8][9][10]. These methods encompass (1) the establishment of a calibration curve that correlates Hounsfield units (HU) with electron densities; this curve can be defined using either an adapted phantom or patient kV-CBCT images.…”

Section: Introductionmentioning

confidence: 99%

Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer

Khamfongkhruea,

Prakarnpilas,

Thongsawad

et al. 2024

Radiat Oncol J

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Purpose: To generate and investigate a supervised deep learning algorithm for creating synthetic computed tomography (sCT) images from kilovoltage cone-beam CT (kV-CBCT) images for adaptive radiation therapy (ART) in head and neck cancer (HNC). Materials and Methods:This study generated the supervised U-Net deep learning model using 3,491 image pairs from planning CT (pCT) and kV-CBCT datasets obtained from 40 HNC patients. The dataset was split into 80% for training and 20% for testing. The evaluation of the sCT images compared to pCT images focused on three aspects: Hounsfield units accuracy, assessed using mean absolute error (MAE) and root mean square error (RMSE); image quality, evaluated using the peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM) between sCT and pCT images; and dosimetric accuracy, encompassing 3D gamma passing rates for dose distribution and percentage dose difference. Results: MAE, RMSE, PSNR, and SSIM showed improvements from their initial values of 53. 15 ± 40.09, 153.99 ± 79.78, 47.91 ± 4.98 dB, and 0.97 ± 0.02 to 41.47 ± 30.59, 130.39 ± 78.06, 49.93 ± 6.00 dB, and 0.98 ± 0.02, respectively. Regarding dose evaluation, 3D gamma passing rates for dose distribution within sCT images under 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria, yielded passing rates of 92.1% ± 3.8%, 93.8% ± 3.0%, and 96.9% ± 2.0%, respectively. The sCT images exhibited minor variations in the percentage dose distribution of the investigated target and structure volumes. However, it is worth noting that the sCT images exhibited anatomical variations when compared to the pCT images. Conclusion: These findings highlight the potential of the supervised U-Net deep learningmodel in generating kV-CBCT-based sCT images for ART in patients with HNC.

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