Online adaptive MR-guided radiotherapy: Conformity of contour adaptation for prostate cancer, rectal cancer and lymph node oligometastases among radiation therapists and radiation oncologists

Rasing, M.; Sikkes, G.G.; Vissers, Nicole G.P.M.; Kotte, A.N.T.J.; Boudewijn, Joske H.; Doornaert, P.; Eppinga, Wietse S.C.; Intven, Martijn; Rutgers, Reijer H.A.; Scheeren, A.; Snoeren, Louk M.W.; Vlig, Tiny B.; Zyp, Jochem R.N. van der Voort van; Wijkhuizen, Lisa M.; Rossum, Peter S.N. van; Peters, Max; Jürgenliemk-Schulz, Ina M.

doi:10.1016/j.tipsro.2022.08.004

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…For specific equations for metric calculations please see corresponding Surface Distances Python package documentation 39 . Resultant values are broadly consistent with previous work in breast 40 , sarcoma 41 , H&N 35,42,43 , GYN 44 , and GI [44][45][46] IOV studies.…”

Section: Segmentation Interobserver Variabilitysupporting

confidence: 89%

Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites

et al. 2023

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Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.

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Section: Segmentation Interobserver Variabilitysupporting

confidence: 89%

Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites

et al. 2023

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“…In a study where a DSC of 0.94 was reported on ten prostate patient contours on T2-weighted MR-images generated across 5 oncologists [6] , and high DSC prostate contour scores were noted between the therapist generated online contour and subsequent single oncologist observer comparison [8] . In other studies, similar to our assessment, adapted bladder and bowel contours between oncologists and therapists resulted in high agreement [16] , [17] . It should be noted that in this study, the cases in phase 1 were stringently scored, akin to prostate and OAR contours generated on an offline reference dataset (e.g.…”

Section: Discussionsupporting

confidence: 79%

Practice-based training strategy for therapist-driven prostate MR-Linac adaptive radiotherapy

Li¹,

Padayachee²,

Navarro³

et al. 2023

Technical Innovations & Patient Support in Radiation Oncolo

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“…Radiation oncologists must be fully aware of the tumor and OAR for radiotherapy that a patient will receive, especially since re-segmentation is very time-consuming. Recently published study have been allowed technicians to re-segment OAR and the tumor in online ART of MR-Linac ( 27 ). In any case, staff education is important for smooth treatment by online ART.…”

Section: Problems With and Limitations Of Mr-linacmentioning

confidence: 99%

Beginning of clinical treatment using the 1.5 Tesla MR-Linac system in Japan: a narrative review

Takahashi,

Tanaka,

Umezawa

et al. 2024

Transl Cancer Res

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Background and Objective In the field of radiation therapy, image-guided radiotherapy (IGRT) technology has been gradually improving and highly accurate radiation treatment has been possible. Research on IGRT using 1.5 Tesla magnetic resonance imaging (MRI) began in 1999, and a radiation therapy device called 1.5 Tesla magnetic resonance linear accelerator (MR-Linac), which combines a linear accelerator with 1.5 Tesla MRI, was developed in Europe. The aim of this review is to present an overview of 1.5 Tesla MR-Linac with a review of the literature and our experience. Methods Reports related to 1.5 Tesla MR-Linac were searched for in PubMed and are discussed in relation to our experience. Key Content and Findings The 1.5 Tesla MR-Linac enables IGRT using 1.5 Tesla MRI, further enhancing the precision of radiation therapy. Position verification by cone-beam computed tomography (CBCT) is performed in many institutions, but soft tissue contrast is often unclear in CBCT images of the abdomen and mediastinal organs. Since the 1.5 Tesla MR-Linac allows position verification using MRI, position verification can be performed using clear MRI even in regions where CBCT is unclear. With the 1.5 Tesla MR-Linac, it is possible to perform online adaptive radiotherapy (ART) using 1.5 Tesla MRI. Online ART is a method in which images are acquired while the patient is on the treatment table. The method is based on the current condition of the organs in the body on that day and an optimal treatment field is recreated. Additionally, it allows monitoring of tumor motion using cine images obtained by 1.5 Tesla MRI during the delivery of X-ray radiation. A previous report showed that patients with prostate cancer who received radiotherapy by MR-Linac had fewer side effects than those in patients who received conventional CBCT radiation therapy. Conclusions The 1.5 Tesla MR-Linac obtained CE-mark certification in Europe in August 2018 and it has been used for clinical treatment. In Japan, clinical treatment using this device started in 2021. By using 1.5 Tesla MR-Linac, patients can be provided with higher precision radiotherapy. In this review, we provide an overview of 1.5 Tesla MR-Linac.

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Online adaptive MR-guided radiotherapy: Conformity of contour adaptation for prostate cancer, rectal cancer and lymph node oligometastases among radiation therapists and radiation oncologists

Cited by 14 publications

References 24 publications

Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites

Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites

Practice-based training strategy for therapist-driven prostate MR-Linac adaptive radiotherapy

Beginning of clinical treatment using the 1.5 Tesla MR-Linac system in Japan: a narrative review

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