Organ at risk delineation for radiation therapy clinical trials: Global Harmonization Group consensus guidelines

Mir, Romaana; Kelly, Sarah; Xiao, Ying; Moore, Alisha; Clark, Catharine H.; Clementel, E.; Corning, C.; Ebert, Martin A.; Hoskin, Peter; Hurkmans, Coen; Ishikura, Satoshi; Kristensen, Ingrid; Kry, Stephen F; Lehmann, Jöerg; Michalski, Jeff M.; Monti, A.F.; Nakamura, Mitsuhiro; Thompson, Kenton; Yang, Haidong; Zubizarreta, Eduardo; Andratschke, Nicolaus; Miles, Elizabeth

doi:10.1016/j.radonc.2020.05.038

Cited by 67 publications

(42 citation statements)

References 39 publications

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“…We have provided support to 243 clinical trial participants in nine JCOG radiotherapy multicenter clinical trials. Furthermore, we are also conducting international joint research in collaboration with the global QA of radiation therapy clinical trials harmonization group (GHG), which is described later in this report [ 29 , 30 , 31 ].…”

Section: Construction and Activity Of Jcog–rtsg–mpwgmentioning

confidence: 99%

An overview of the medical-physics-related verification system for radiotherapy multicenter clinical trials by the Medical Physics Working Group in the Japan Clinical Oncology Group–Radiation Therapy Study Group

Nishio

Nakamura

Okamoto

et al. 2020

Journal of Radiation Research

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The Japan Clinical Oncology Group–Radiation Therapy Study Group (JCOG-RTSG) has initiated several multicenter clinical trials for high-precision radiotherapy, which are presently ongoing. When conducting multi-center clinical trials, a large difference in physical quantities, such as the absolute doses to the target and the organ at risk, as well as the irradiation localization accuracy, affects the treatment outcome. Therefore, the differences in the various physical quantities used in different institutions must be within an acceptable range for conducting multicenter clinical trials, and this must be verified with medical physics consideration. In 2011, Japan’s first Medical Physics Working Group (MPWG) in the JCOG-RTSG was established to perform this medical-physics-related verification for multicenter clinical trials. We have developed an auditing method to verify the accuracy of the absolute dose and the irradiation localization. Subsequently, we credentialed the participating institutions in the JCOG multicenter clinical trials that were using stereotactic body radiotherapy (SBRT) for lungs, intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) for several disease sites, and proton beam therapy (PT) for the liver. From the verification results, accuracies of the absolute dose and the irradiation localization among the participating institutions of the multicenter clinical trial were assured, and the JCOG clinical trials could be initiated.

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Section: Construction and Activity Of Jcog–rtsg–mpwgmentioning

confidence: 99%

An overview of the medical-physics-related verification system for radiotherapy multicenter clinical trials by the Medical Physics Working Group in the Japan Clinical Oncology Group–Radiation Therapy Study Group

Nishio

Nakamura

Okamoto

et al. 2020

Journal of Radiation Research

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“…In addition, the supraclavicular lymph nodes were additionally delineated according to the RTOG guidelines (CTVn_SCL RTOG) [25]. The OARs included the heart, right and left lungs, esophagus, spinal cord, and thyroid [26].…”

Section: Delineationmentioning

confidence: 99%

Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

et al. 2021

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Background In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentation for treatment planning increases physicians’ workload and inter-physician variability considerably. In this study, we evaluated the potential benefits of deep learning-based auto-segmented contours by comparing them to manually delineated contours for breast cancer patients. Methods CTVs for bilateral breasts, regional lymph nodes, and OARs (including the heart, lungs, esophagus, spinal cord, and thyroid) were manually delineated on planning computed tomography scans of 111 breast cancer patients who received breast-conserving surgery. Subsequently, a two-stage convolutional neural network algorithm was used. Quantitative metrics, including the Dice similarity coefficient (DSC) and 95% Hausdorff distance, and qualitative scoring by two panels from 10 institutions were used for analysis. Inter-observer variability and delineation time were assessed; furthermore, dose-volume histograms and dosimetric parameters were also analyzed using another set of patient data. Results The correlation between the auto-segmented and manual contours was acceptable for OARs, with a mean DSC higher than 0.80 for all OARs. In addition, the CTVs showed favorable results, with mean DSCs higher than 0.70 for all breast and regional lymph node CTVs. Furthermore, qualitative subjective scoring showed that the results were acceptable for all CTVs and OARs, with a median score of at least 8 (possible range: 0–10) for (1) the differences between manual and auto-segmented contours and (2) the extent to which auto-segmentation would assist physicians in clinical practice. The differences in dosimetric parameters between the auto-segmented and manual contours were minimal. Conclusions The feasibility of deep learning-based auto-segmentation in breast RT planning was demonstrated. Although deep learning-based auto-segmentation cannot be a substitute for radiation oncologists, it is a useful tool with excellent potential in assisting radiation oncologists in the future. Trial registration Retrospectively registered.

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“…It is essential to determine the precise radiation dose received by each cardiac substructure and its potential cardiovascular toxicity risk [119,120]. It is, therefore, recommendable to incorporate new consensual contour atlases into routine clinical practice, for which self-segmentation computer tools may be of help, to ensure the homogeneity of the dosimetric data compiled [121].…”

Section: Future Directionsmentioning

confidence: 99%

Breast Radiotherapy-Related Cardiotoxicity. When, How, Why. Risk Prevention and Control Strategies

Díaz-Gavela

Figueiras-Graillet²,

Ángel

et al. 2021

Cancers

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In recent decades, improvements in breast cancer management have increased overall patient survival; however, many cancer therapies have been linked to an important risk of cardiovascular adverse events. Cardio-oncology has been proposed as an emerging specialty to coordinate preventive strategies that improve the cardiovascular health of oncologic patients. It employs the most suitable personalized multidisciplinary management approach for each patient to optimize their cardiovascular health and improve their survival and quality of life. Radiotherapy is an essential part of the therapeutic regimen in breast cancer patients but can also increase the risk of cardiovascular disease. Therefore, minimizing the negative impact of radiation therapy is an important challenge for radiotherapy oncologists and cardiologists specializing in this field. The aim of the present review is to update our knowledge about radiation-induced cardiotoxicity in breast cancer patients by undertaking a critical review of the relevant literature to determine risk prevention and control strategies currently available.

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Organ at risk delineation for radiation therapy clinical trials: Global Harmonization Group consensus guidelines

Cited by 67 publications

References 39 publications

An overview of the medical-physics-related verification system for radiotherapy multicenter clinical trials by the Medical Physics Working Group in the Japan Clinical Oncology Group–Radiation Therapy Study Group

An overview of the medical-physics-related verification system for radiotherapy multicenter clinical trials by the Medical Physics Working Group in the Japan Clinical Oncology Group–Radiation Therapy Study Group

Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

Breast Radiotherapy-Related Cardiotoxicity. When, How, Why. Risk Prevention and Control Strategies

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