Uncertainties and CTV to PTV margins quantitative assessment using cone-beam CT technique in clinical application for prostate, and head and neck irradiation tumours

Juan-Senabre, X.J.; López-Tarjuelo, Juan; Conde-Moreno, Antonio J.; Santos-Serra, Agustín; Sánchez-Iglesias, A.L.; Quirós-Higueras, Juan David; Blancas, N. De Marco; Calzada-Feliu, S.; Ferrer-Albiach, Carlos

doi:10.1007/s12094-011-0740-8

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…44 Similar margins of 7.3, 7.0 and 9.0 mm in the LR, SI and AP directions were proposed for the prostate gland by quantitative assessment based on CBCT data. 45 Target dose coverage was evaluated by the proportion of the CTV encompassed by the 95% isodose for two options of 10-and 7-mm safety margins by Paluska et al 46 Kliton et al 47 suggested 9.3-, 6.5-and 8.9-mm PTV margins in the LR, SI and AP directions if in-room kV CT is used for IG. The pre-treatment CBCT images of radio-opaque markers provided information about the motion of the target immediately preceding the treatment useful for PTV margin evaluation, 48 but no correlation of these data with pre-and post-CBCT data comparison was found.…”

Section: Use Of Daily Ultrasoundmentioning

confidence: 99%

Target margins in radiotherapy of prostate cancer

Yartsev

2016

BJR

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We reviewed the literature on the use of margins in radiotherapy of patients with prostate cancer, focusing on different options for image guidance (IG) and technical issues. The search in PubMed database was limited to include studies that involved external beam radiotherapy of the intact prostate. Post-prostatectomy studies, brachytherapy and particle therapy were excluded. Each article was characterized according to the IG strategy used: positioning on external marks using room lasers, bone anatomy and soft tissue match, usage of fiducial markers, electromagnetic tracking and adapted delivery. A lack of uniformity in margin selection among institutions was evident from the review. In general, introduction of pre- and in-treatment IG was associated with smaller planning target volume (PTV) margins, but there was a lack of definitive experimental/clinical studies providing robust information on selection of exact PTV values. In addition, there is a lack of comparative research regarding the cost-benefit ratio of the different strategies: insertion of fiducial markers or electromagnetic transponders facilitates prostate gland localization but at a price of invasive procedure; frequent pre-treatment imaging increases patient in-room time, dose and labour; online plan adaptation should improve radiation delivery accuracy but requires fast and precise computation. Finally, optimal protocols for quality assurance procedures need to be established.

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Section: Use Of Daily Ultrasoundmentioning

confidence: 99%

Target margins in radiotherapy of prostate cancer

Yartsev

2016

BJR

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“…Translational shifts in the prostate during the course of radiotherapy have been reported. For instance, Meijer et al (5) reported shifts of 2 to 4 mm, and Juan‐Senabre et al (6) reported shifts of 7 to 9 mm, in the position of the prostate during a radiotherapy course.…”

Section: Introductionmentioning

confidence: 99%

Determination of optimal PTV margin for patients receiving CBCT‐guided prostate IMRT: comparative analysis based on CBCT dose calculation with four different margins

Gill

Reddy

Campbell

et al. 2015

J Applied Clin Med Phys

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Variations in daily setup and rectum/bladder filling lead to uncertainties in the delivery of prostate IMRT. The purpose of this study is to determine the optimal PTV margin for CBCT‐guided prostate IMRT based on daily CBCT dose calculations using four different margins. Five patients diagnosed with low‐risk prostate cancer were treated with prostate IMRT to 70 Gy in 28 fractions using daily CBCT for image guidance. The prostate CTV and OARs were contoured on all CBCTs. IMRT plans were created using 1 mm, 3 mm, 5 mm, and 7 mm CTV to PTV expansions. For each delivered fraction, dose calculations were generated utilizing the pretreatment CBCT translational shifts performed and dosimetric analysis was performed. One hundred and forty total treatment fractions (CBCT sessions) were evaluated. The planned prostate CTV V100% was 100% for all PTV margins. Based on CBCT analysis, the actual cumulative CTVs V100% were 96.55%±2.94%,99.49%±1.36%,99.98%±0.26%, and 99.99%±0.05% for 1, 3, 5, and 7 mm uniform PTV margins, respectively. Delivered rectum and bladder doses were different as compared to expected planned doses, with the magnitude of differences increasing with PTV margin. Daily setup variation during prostate IMRT yields differences in the actual vs. expected doses received by the prostate CTV, rectum, and bladder. The magnitude of these differences is significantly affected by the PTV margin utilized. It was found that when daily CBCT was used for soft‐tissue alignment of the prostate, a 3 mm PTV margin allowed for CTV to be covered for 99% of cases.PACS numbers: 87.55.dk‐, 87.57.Q‐

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“…In a clinical environment, our cGAN segmentation method would detect and segment the GTV in kV images acquired during RT fractions. However, intrafraction motion during treatment would make it difficult to establish an accurate GTV location in any kV images acquired during treatment 9,10 . We view this study as a necessary but not sufficient study toward the long‐term goal of clinical implementation.…”

Section: Discussionmentioning

confidence: 95%

“…To minimize the dose delivered to healthy organs, H&N cancer patients are required to wear a skin‐tight immobilization mask that minimizes patient motion 7,8 . However, despite the restrictive nature of immobilization masks, motion in the order of several millimeters of the tumor and surrounding tissue can still occur during and between treatment fractions 9,10 . This motion can be caused by a change in the mask's fit due to patient weight loss between fractions, imperfections in the mask manufacturing and fitting process, tumor shrinkage, or treatment‐related oedema 7 .…”

Section: Introductionmentioning

confidence: 99%

Realistic CT data augmentation for accurate deep‐learning based segmentation of head and neck tumors in kV images acquired during radiation therapy

et al. 2023

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Background Using radiation therapy (RT) to treat head and neck (H&N) cancers requires precise targeting of the tumor to avoid damaging the surrounding healthy organs. Immobilisation masks and planning target volume margins are used to attempt to mitigate patient motion during treatment, however patient motion can still occur. Patient motion during RT can lead to decreased treatment effectiveness and a higher chance of treatment related side effects. Tracking tumor motion would enable motion compensation during RT, leading to more accurate dose delivery. Purpose The purpose of this paper is to develop a method to detect and segment the tumor in kV images acquired during RT. Unlike previous tumor segmentation methods for kV images, in this paper, a process for generating realistic and synthetic CT deformations was developed to augment the training data and make the segmentation method robust to patient motion. Detecting the tumor in 2D kV images is a necessary step toward 3D tracking of the tumor position during treatment. Method In this paper, a conditional generative adversarial network (cGAN) is presented that can detect and segment the gross tumor volume (GTV) in kV images acquired during H&N RT. Retrospective data from 15 H&N cancer patients obtained from the Cancer Imaging Archive were used to train and test patient‐specific cGANs. The training data consisted of digitally reconstructed radiographs (DRRs) generated from each patient's planning CT and contoured GTV. Training data was augmented by using synthetically deformed CTs to generate additional DRRs (in total 39 600 DRRs per patient or 25 200 DRRs for nasopharyngeal patients) containing realistic patient motion. The method for deforming the CTs was a novel deformation method based on simulating head rotation and internal tumor motion. The testing dataset consisted of 1080 DRRs for each patient, obtained by deforming the planning CT and GTV at different magnitudes to the training data. The accuracy of the generated segmentations was evaluated by measuring the segmentation centroid error, Dice similarity coefficient (DSC) and mean surface distance (MSD). This paper evaluated the hypothesis that when patient motion occurs, using a cGAN to segment the GTV would create a more accurate segmentation than no‐tracking segmentations from the original contoured GTV, the current standard‐of‐care. This hypothesis was tested using the 1‐tailed Mann‐Whitney U‐test. Results The magnitude of our cGAN segmentation centroid error was (mean ± standard deviation) 1.1 ± 0.8 mm and the DSC and MSD values were 0.90 ± 0.03 and 1.6 ± 0.5 mm, respectively. Our cGAN segmentation method reduced the segmentation centroid error (p < 0.001), and MSD (p = 0.031) when compared to the no‐tracking segmentation, but did not significantly increase the DSC (p = 0.294). Conclusions The accuracy of our cGAN segmentation method demonstrates the feasibility of this method for H&N cancer patients during RT. Accurate tumor segmentation of H&N tumors would allow for intrafraction monitoring...

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Uncertainties and CTV to PTV margins quantitative assessment using cone-beam CT technique in clinical application for prostate, and head and neck irradiation tumours

Cited by 11 publications

References 30 publications

Target margins in radiotherapy of prostate cancer

Target margins in radiotherapy of prostate cancer

Determination of optimal PTV margin for patients receiving CBCT‐guided prostate IMRT: comparative analysis based on CBCT dose calculation with four different margins

Realistic CT data augmentation for accurate deep‐learning based segmentation of head and neck tumors in kV images acquired during radiation therapy

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