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

Boekhoff, M.; Bouwmans, R.; Doornaert, P.; Intven, Martijn; Lagendijk, J J W; Lier, Astrid L.H.M.W. van; Rasing, M.; Ven, Saskia van de; Meijer, G.J.; Mook, Stella

doi:10.1016/j.ctro.2022.03.008

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…The relative mean dose to the lungs was less affected by the location of the esophageal cancer, decreasing by 18.04% (cervical and upper thoracic) and 12.36% (middle thoracic and lower thoracic) in the ATS plan, or 0.15 and 0.11 Gy, respectively. The finding is similar to that of Boekhoff et al (30) in their MRgRT study of esophageal cancer, in which the average mean lung dose was reduced by 26%, and the average mean heart dose was reduced by 12%. In summary, as expected, the plan quality of the ATS workflow exceeded that of the ATP workflow since the ATS plan was equivalent to complete replanning.…”

Section: Discussionsupporting

confidence: 89%

Feasibility study of adaptive radiotherapy for esophageal cancer using artificial intelligence autosegmentation based on MR-Linac

Wang¹,

Liu²,

Song³

et al. 2023

Front. Oncol.

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ObjectiveWe proposed a scheme for automatic patient-specific segmentation in Magnetic Resonance (MR)-guided online adaptive radiotherapy based on daily updated, small-sample deep learning models to address the time-consuming delineation of the region of interest (ROI) in the adapt-to-shape (ATS) workflow. Additionally, we verified its feasibility in adaptive radiation therapy for esophageal cancer (EC).MethodsNine patients with EC who were treated with an MR-Linac were prospectively enrolled. The actual adapt-to-position (ATP) workflow and simulated ATS workflow were performed, the latter of which was embedded with a deep learning autosegmentation (AS) model. The first three treatment fractions of the manual delineations were used as input data to predict the next fraction segmentation, which was modified and then used as training data to update the model daily, forming a cyclic training process. Then, the system was validated in terms of delineation accuracy, time, and dosimetric benefit. Additionally, the air cavity in the esophagus and sternum were added to the ATS workflow (producing ATS+), and the dosimetric variations were assessed.ResultsThe mean AS time was 1.40 [1.10–1.78 min]. The Dice similarity coefficient (DSC) of the AS model gradually approached 1; after four training sessions, the DSCs of all ROIs reached a mean value of 0.9 or more. Furthermore, the planning target volume (PTV) of the ATS plan showed a smaller heterogeneity index than that of the ATP plan. Additionally, V5 and V10 in the lungs and heart were greater in the ATS+ group than in the ATS group.ConclusionThe accuracy and speed of artificial intelligence–based AS in the ATS workflow met the clinical radiation therapy needs of EC. This allowed the ATS workflow to achieve a similar speed to the ATP workflow while maintaining its dosimetric advantage. Fast and precise online ATS treatment ensured an adequate dose to the PTV while reducing the dose to the heart and lungs.

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Section: Discussionsupporting

confidence: 89%

Feasibility study of adaptive radiotherapy for esophageal cancer using artificial intelligence autosegmentation based on MR-Linac

Wang¹,

Liu²,

Song³

et al. 2023

Front. Oncol.

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“…Adaptive radiotherapy technologies, including the use of magnetic resonance linear accelerators, are also under investigation for esophageal cancer, and could help spare organs from radiation. 191 These technologies are of potential importance, given recognition of the importance of radiation related cardiac and pulmonary toxicity to surgical and long term outcomes, in addition to evidence for sequalae, such as functional hyposplenism. 192 193 Proton beam therapy is a potential alternative to photon based treatment strategies.…”

Section: Radiation Treatmentmentioning

confidence: 99%

Advances in diagnosis and management of cancer of the esophagus

Deboever,

Jones,

Yamashita

et al. 2024

BMJ

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Esophageal cancer is the seventh most common malignancy worldwide, with over 470 000 new cases diagnosed each year. Two distinct histological subtypes predominate, and should be considered biologically separate disease entities.1 These subtypes are esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC). Outcomes remain poor regardless of subtype, with most patients presenting with late stage disease.2 Novel strategies to improve early detection of the respective precursor lesions, squamous dysplasia, and Barrett’s esophagus offer the potential to improve outcomes. The introduction of a limited number of biologic agents, as well as immune checkpoint inhibitors, is resulting in improvements in the systemic treatment of locally advanced and metastatic esophageal cancer. These developments, coupled with improvements in minimally invasive surgical and endoscopic treatment approaches, as well as adaptive and precision radiotherapy technologies, offer the potential to improve outcomes still further. This review summarizes the latest advances in the diagnosis and management of esophageal cancer, and the developments in understanding of the biology of this disease.

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“…Nine esophageal cancer patients received neoadjuvant chemoradiotherapy treatment on a 1.5T MR-Linac (Elekta Unity, Elekta AB, Stockholm, Sweden) between July 2019 and March 2021, as previously reported by our group [113]. All patients consented to the MOMENTUM study (NCT04075305), which has been approved by the Medical Research Ethics Committee of the University Medical Centre Utrecht in the Netherlands [108].…”

Section: Methodsmentioning

confidence: 99%

“…Optimal radiotherapy delivery accounts for day-to-day changes in target volumes. The introduction of magnetic resonance (MR) guided radiotherapy (MRgRT) has allowed for online plan adaptation of the treatment plan based on MRI visualization of the daily anatomy [22,24,113]. In a recent study we have demonstrated in a cohort of patients with esophageal cancer that MRgRT reduces the dose to organs at risk (OAR) when daily plan adaptation is applied in combination with the use of smaller treatment margins [103,113].…”

Section: Introductionmentioning

confidence: 99%

“…The introduction of magnetic resonance (MR) guided radiotherapy (MRgRT) has allowed for online plan adaptation of the treatment plan based on MRI visualization of the daily anatomy [22,24,113]. In a recent study we have demonstrated in a cohort of patients with esophageal cancer that MRgRT reduces the dose to organs at risk (OAR) when daily plan adaptation is applied in combination with the use of smaller treatment margins [103,113]. Daily plan adaptation compensates for set-up inaccuracies and interfraction tumor changes, leaving only intrafraction motion (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Clinical introduction of MR-guided radiotherapy for esophageal cancer

Boekhoff

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Radiotherapy for esophageal cancer requires the use of imaging to localize and visualize the tumor. This thesis describes the use of magnetic resonance imaging in the radiotherapy treatment of patients with esophageal cancer. The first part of this thesis showed that esophageal cancer tumors are subject to position and shape changes throughout the treatment period. We have calculated that a treatment margin of 8-10mm would have been sufficient to provide sufficient target coverage for most patients. However, some patients showed extreme interfraction motion, which indicates the need for adaptive treatment strategies. The second part of this thesis reported on our experiences with an online adaptive, MR-guided radiotherapy treatment workflow. This adaptive strategy used day-to-day plan adaptation with smaller treatment margins of 6 mm. We have shown that this workflow is moderately feasible, mainly caused by the long treatment time of up to one hour per treatment session. We concluded that optimization of the presented clinical workflow is required. Nevertheless, the adaptive treatment strategy resulted in a dose reduction to the organs-at-risk compared to conventional treatment. This might allow for employment of improved treatment strategies, such as boosting strategies, to use this dose reduction to potentially improve the result of radiotherapy treatments of esophageal cancer tumors.

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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

Cited by 7 publications

References 32 publications

Feasibility study of adaptive radiotherapy for esophageal cancer using artificial intelligence autosegmentation based on MR-Linac

Feasibility study of adaptive radiotherapy for esophageal cancer using artificial intelligence autosegmentation based on MR-Linac

Advances in diagnosis and management of cancer of the esophagus

Clinical introduction of MR-guided radiotherapy for esophageal cancer

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