Setup Error Assessment and Correction in Planar kV Image- Versus Cone Beam CT Image-Guided Radiation Therapy: A Clinical Study of Early Breast Cancer Treated With External Beam Partial Breast Irradiation

Wang, Wei; Yu, Ting; Xu, Min; Shao, Qing; Zhang, Yingjie; Li, Jianbin

doi:10.1177/1533033819853847

Cited by 6 publications

(6 citation statements)

References 35 publications

(61 reference statements)

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“…However, VMAT-5B appeared to be more robust than VMAT-10B because slight CTV underdosage and acceptable increased dose to the OARs were noted in this group. Reportedly, couch shifts in patient setup were impossible to eliminate and the mean magnitudes were generally between 1 mm and 5 mm with imaging guidance [14][15][16]. Similar to previous data [14,16], we found ~3 mm setup errors in all directions (unpublished) in a group of patients who underwent mastectomy and immediate implant-based reconstruction using weekly cone-beam CT (CBCT).…”

Section: Discussionsupporting

confidence: 81%

“…Inter-fractional patient setup errors are another source of dose variability. Generally, the errors are in the order of several millimeters when pretreatment setup verifications have been routinely performed [14][15][16]. Considering the high modulations and steep dose fall-off in VMAT, a tiny setup error of several millimeters is capable to induce significant dose loss in the target [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Robustness of VMAT to setup errors in postmastectomy radiotherapy of left-sided breast cancer: Impact of bolus thickness

Chen

Gao

et al. 2023

PLoS ONE

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Background Volumetric modulated arc therapy (VMAT) with varied bolus thicknesses has been employed in postmastectomy radiotherapy (PMRT) of breast cancer to improve superficial target coverage. However, impact of bolus thickness on plan robustness remains unclear. Methods The study enrolled ten patients with left-sided breast cancer who received radiotherapy using VMAT with 5 mm and 10 mm bolus (VMAT-5B and VMAT-10B). Inter-fractional setup errors were simulated by introducing a 3 mm shift to isocenter of the original plans in the anterior-posterior, left-right, and inferior-superior directions. The plans (perturbed plans) were recalculated without changing other parameters. Dose volume histograms (DVH) were collected for plan evaluation. Absolute dose differences in DVH endpoints for the clinical target volume (CTV), heart, and left lung between the perturbed plans and the original ones were used for robustness analysis. Results VMAT-10B showed better target coverage, while VMAT-5B was superior in organs-at-risk (OARs) sparing. As expected, small setup errors of 3 mm could induce dose fluctuations in CTV and OARs. The differences in CTV were small in VMAT-5B, with a maximum difference of -1.05 Gy for the posterior shifts. For VMAT-10B, isocenter shifts in the posterior and right directions significantly decreased CTV coverage. The differences were -1.69 Gy, -1.48 Gy and -1.99 Gy, -1.69 Gy for ΔD95% and ΔD98%, respectively. Regarding the OARs, only isocenter shifts in the posterior, right, and inferior directions increased dose to the left lung and the heart. Differences in VMAT-10B were milder than those in VMAT-5B. Specifically, mean heart dose were increased by 0.42 Gy (range 0.10 ~ 0.95 Gy) and 0.20 Gy (range -0.11 ~ 0.72 Gy), and mean dose for the left lung were increased by 1.02 Gy (range 0.79 ~ 1.18 Gy) and 0.68 Gy (range 0.47 ~ 0.84 Gy) in VMAT-5B and VMAT-10B, respectively. High-dose volumes in the organs were increased by approximate 0 ~ 2 and 1 ~ 3 percentage points, respectively. Nevertheless, most of the dosimetric parameters in the perturbed plans were still clinically acceptable. Conclusions VMAT-5B appears to be more robust to 3 mm setup errors than VMAT-10B. VMAT-5B also resulted in better OARs sparing with acceptable target coverage and dose homogeneity. Therefore 5 mm bolus is recommended for PMRT of left-sided breast cancer using VMAT.

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Section: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Robustness of VMAT to setup errors in postmastectomy radiotherapy of left-sided breast cancer: Impact of bolus thickness

Chen

Gao

et al. 2023

PLoS ONE

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“…However, the benefits of daily IGRT need to be balanced against the costs, added time, doses and institutional-based PTV margins. Additionally, our results showed that residual setup errors remain present after the daily online IGRT correction because rigid registration fails to correct target deformation and anatomical motion of normal tissues and target during treatment [35] .…”

Section: Discussionmentioning

confidence: 87%

Comparing treatment uncertainty for ultra- vs. standard-hypofractionated breast radiation therapy based on in-vivo dosimetry

Fiagan

Bossuyt

Machiels

et al. 2022

Physics and Imaging in Radiation Oncology

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“…These artifacts can lead to inaccurate dose calculations and impair the ability to accurately target the tumor, which can compromise the success of the radiotherapy treatment. Secondly, CBCT images have a field of view that is not typically large enough to include the entire patient’s cross-section, leading to part of the patient’s anatomy not being captured particularly if the tumour is lateralised [ 18 – 20 ].…”

Section: Introductionmentioning

confidence: 99%

Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept

Shields

Prabhakar

2023

Phys Eng Sci Med

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The patient setup technique currently in practice in most radiotherapy departments utilises on-couch cone-beam computed tomography (CBCT) imaging. Patients are positioned on the treatment couch using visual markers, followed by fine adjustments to the treatment couch position depending on the shift observed between the computed tomography (CT) image acquired for treatment planning and the CBCT image acquired immediately before commencing treatment. The field of view of CBCT images is limited to the size of the kV imager which leads to the acquisition of partial CBCT scans for lateralised tumors. The cone-beam geometry results in high amounts of streaking artifacts and in conjunction with limited anatomical information reduces the registration accuracy between planning CT and the CBCT image. This study proposes a methodology that can improve radiotherapy patient setup CBCT images by removing streaking artifacts and generating the missing patient anatomy with patient-specific precision. This research was split into two separate studies. In Study A, synthetic CBCT (sCBCT) data was created and used to train two machine learning models, one for removing streaking artifacts and the other for generating the missing patient anatomy. In Study B, planning CT and on-couch CBCT data from several patients was used to train a base model, from which a transfer of learning was performed using imagery from a single patient, producing a patient-specific model. The models developed for Study A performed well at removing streaking artifacts and generating the missing anatomy. The outputs yielded in Study B show that the model understands the individual patient and can generate the missing anatomy from partial CBCT datasets. The outputs generated demonstrate that there is utility in the proposed methodology which could improve the patient setup and ultimately lead to improving overall treatment quality.

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Setup Error Assessment and Correction in Planar kV Image- Versus Cone Beam CT Image-Guided Radiation Therapy: A Clinical Study of Early Breast Cancer Treated With External Beam Partial Breast Irradiation

Cited by 6 publications

References 35 publications

Robustness of VMAT to setup errors in postmastectomy radiotherapy of left-sided breast cancer: Impact of bolus thickness

Robustness of VMAT to setup errors in postmastectomy radiotherapy of left-sided breast cancer: Impact of bolus thickness

Comparing treatment uncertainty for ultra- vs. standard-hypofractionated breast radiation therapy based on in-vivo dosimetry

Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept

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