CTV-to-PTV margin assessment for esophageal cancer radiotherapy based on an accumulated dose analysis

Boekhoff, M.; Defize, Ingmar L.; Borggreve, Alicia S; Hillegersberg, Richard van; Kotte, A.N.T.J.; Lagendijk, J J W; Lier, Astrid L.H.M.W. van; Ruurda, Jelle P.; Takahashi, Noriyoshi; Mook, Stella; Meijer, G.J.

doi:10.1016/j.radonc.2021.05.005

Cited by 8 publications

(11 citation statements)

References 24 publications

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“…Fourthly, small registration errors could have impacted the accumulated dose distribution. However, the accuracy of the image registration was high (dice coefficient of 0.91 ± 0.02) and therefore we believe that the impact of the uncertainty on the results is modest, as the dose distribution for each patient is calculated from the accumulated dose of five registrations, each with a separate error [13]. Furthermore, with the addition of a bending penalty metric the deformation vector field in the organs at risk is distributed more evenly and less subject to unexpected deformations.…”

Section: Discussionmentioning

confidence: 99%

“…To ensure correct mapping of the CTV, a second registration step was performed. This final registration step consisted of a mask match of the delineated CTVs (kappa statistic metric), which again was also combined with a bending penalty metric 4 [13].…”

Section: Treatment Simulationmentioning

confidence: 99%

“…To account for geometric uncertainties such as the daily variability of the CTV in relation to the bony anatomy and breathing motion, the CTV is expanded to a planning target volume (PTV). Recent studies have suggested an expansion of 7-12 mm in different directions, resulting in PTVs that are approximately three times the volume of CTVs [3][4][5]13]. These large PTVs inherently result in large high and intermediate dose levels at nearby organs-at-risk (OARs) such as the lungs and heart.…”

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confidence: 99%

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An in-silico assessment of the dosimetric benefits of MR-guided radiotherapy for esophageal cancer patients

Boekhoff

Defize

Borggreve

et al. 2021

Radiotherapy and Oncology

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To assess the dosimetric benefits of online MR-guided radiotherapy (MRgRT) for esophageal cancer patients and to assess how these benefits could be translated into a local boosting strategy to improve future outcomes. Methods: Twenty-nine patients were in-silico treated with both a MRgRT regimen and a conventional image guided radiotherapy (IGRT) regimen using dose warping techniques. Here, the inter and intrafractional changes that occur over the course of treatment (as derived from 5 MRI scans that were acquired weekly during treatment) were incorporated to assess the total accumulated dose for each regimen. Results: A significant reduction in dose to the organs-at-risk (OARs) was observed for all dose-volumehistogram (DVH) parameters for the MRgRT regimen without concessions to target coverage compared to the IGRT regimen. The mean lung dose was reduced by 28%, from 7.9 to 5.7 Gy respectively and V20Gy of the lungs was reduced by 55% (6.3-2.8%). A reduction of 24% was seen in mean heart dose (14.8-11.2 Gy), while the V25Gy of the heart was decreased by 53% (14.3-6.7%) and the V40Gy of the heart was decreased by 69% (3.9-1.2%). In addition, MRgRT dose escalation regimens with a boost up to 66% of the prescription dose to the primary tumor yielded approximately the same dose levels to the OARs as from the conventional IGRT regimen. Conclusion:This study revealed that MRgRT for esophageal cancer has the potential to significantly reduce the dose to heart and lungs. In addition, online high precision targeting of the primary tumor opens new perspectives for local boosting strategies to improve outcome of the local management of this disease.

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Section: Discussionmentioning

confidence: 99%

Section: Treatment Simulationmentioning

confidence: 99%

mentioning

confidence: 99%

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An in-silico assessment of the dosimetric benefits of MR-guided radiotherapy for esophageal cancer patients

Boekhoff

Defize

Borggreve

et al. 2021

Radiotherapy and Oncology

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“…To account for these patient positioning and other inaccuracies the CTV is expanded to a relatively large planning target volume (PTV). Recent studies have suggested margins varying between 7 mm and 12 mm for different directions, resulting in PTVs that are about three times the volume of the CTV [11][12][13][14]. Online MRI provides excellent soft tissue contrast, thereby enabling accurate target definition for each fraction and with online plan adaptation, interfractional variations (including potential tumor shrinkage) can be corrected for [15].…”

Section: Introductionmentioning

confidence: 99%

Clinical implementation and feasibility of long-course fractionated MR-guided chemoradiotherapy for patients with esophageal cancer: An R-IDEAL stage 1b/2a evaluation of technical innovation

Boekhoff¹,

Bouwmans²,

Doornaert³

et al. 2022

Clinical and Translational Radiation Oncology

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“…Boekhoff et al. [ 1 ] used T2-weighted phase MRI to find that 27 of 29 patients with esophageal cancer could achieve dose coverage by simulating an online bone-match image-guided radiotherapy treatment for each patient, whereas the other 2 patients needed to be rescheduled to achieve dose coverage due to extreme interfractional changes in esophageal position. Defize et al.…”

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confidence: 99%

Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy

Wang

Liu

Kong³

et al. 2023

Strahlenther Onkol

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Objective This study aimed to improve the image quality and CT Hounsfield unit accuracy of daily cone-beam computed tomography (CBCT) using registration generative adversarial networks (RegGAN) and apply synthetic CT (sCT) images to dose calculations in radiotherapy. Methods The CBCT/planning CT images of 150 esophageal cancer patients undergoing radiotherapy were used for training (120 patients) and testing (30 patients). An unsupervised deep-learning method, the 2.5D RegGAN model with an adaptively trained registration network, was proposed, through which sCT images were generated. The quality of deep-learning-generated sCT images was quantitatively compared to the reference deformed CT (dCT) image using mean absolute error (MAE), root mean square error (RMSE) of Hounsfield units (HU), and peak signal-to-noise ratio (PSNR). The dose calculation accuracy was further evaluated for esophageal cancer radiotherapy plans, and the same plans were calculated on dCT, CBCT, and sCT images. Results The quality of sCT images produced by RegGAN was significantly improved compared to the original CBCT images. ReGAN achieved image quality in the testing patients with MAE sCT vs. CBCT: 43.7 ± 4.8 vs. 80.1 ± 9.1; RMSE sCT vs. CBCT: 67.2 ± 12.4 vs. 124.2 ± 21.8; and PSNR sCT vs. CBCT: 27.9 ± 5.6 vs. 21.3 ± 4.2. The sCT images generated by the RegGAN model showed superior accuracy on dose calculation, with higher gamma passing rates (93.3 ± 4.4, 90.4 ± 5.2, and 84.3 ± 6.6) compared to original CBCT images (89.6 ± 5.7, 85.7 ± 6.9, and 72.5 ± 12.5) under the criteria of 3 mm/3%, 2 mm/2%, and 1 mm/1%, respectively. Conclusion The proposed deep-learning RegGAN model seems promising for generation of high-quality sCT images from stand-alone thoracic CBCT images in an efficient way and thus has the potential to support CBCT-based esophageal cancer adaptive radiotherapy.

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CTV-to-PTV margin assessment for esophageal cancer radiotherapy based on an accumulated dose analysis

Cited by 8 publications

References 24 publications

An in-silico assessment of the dosimetric benefits of MR-guided radiotherapy for esophageal cancer patients

An in-silico assessment of the dosimetric benefits of MR-guided radiotherapy for esophageal cancer patients

Clinical implementation and feasibility of long-course fractionated MR-guided chemoradiotherapy for patients with esophageal cancer: An R-IDEAL stage 1b/2a evaluation of technical innovation

Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy

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