Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Vandewinckele, Liesbeth; Claessens, Michaël; Dinkla, Anna M.; Brouwer, Charlotte L.; Crijns, Wouter; Verellen, Dirk; Elmpt, Wouter van

doi:10.1016/j.radonc.2020.09.008

Cited by 186 publications

(135 citation statements)

References 135 publications

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“…Although the majority of commercially available clinical tools still use atlas-based contouring [8] , [9] , a number of vendors are now offering deep-learning contouring (DLC) [10] , [11] . A first step in the process to clinical deployment is validation and commissioning by institutions [12] , [13] , and a number of validation experiments have been published to support clinical implementation [3] , [4] . After the commissioning and the clinical implementation phase, the quality assurance (QA) phase, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Brouwer

Boukerroui

Oliveira

et al. 2020

Physics and Imaging in Radiation Oncology

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Background and purpose: Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring. Materials and methods: A total of 103 clinical head and neck cancer cases, contoured using a commercial deeplearning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019-April 2020). The amount of adjustment performed was calculated, and all cases were registered to a common reference frame for assessment purposes. The median, 10th and 90th percentile of adjustment were calculated and displayed using 3D renderings of structures to visually assess systematic and random adjustment. Results were also compared to inter-observer variation reported previously. Assessment was performed for both the whole structures and for regional sub-structures, and according to the radiation therapy technologist (RTT) who edited the contour. Results: The median amount of adjustment was low for all structures (<2 mm), although large local adjustment was observed for some structures. The median was systematically greater or equal to zero, indicating that the auto-contouring tends to under-segment the desired contour. Conclusion: Auto-contouring performance assessment in routine clinical practice has identified systematic improvements required technically, but also highlighted the need for continued RTT training to ensure adherence to guidelines.

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Section: Introductionmentioning

confidence: 99%

“…After the commissioning and the clinical implementation phase, the quality assurance (QA) phase, i.e. ‘model monitoring’ or ‘post-market surveillance’, is needed [13] . This includes the monitoring of user interactions in routine clinical use to determine the true performance impact of the model.…”

Section: Introductionmentioning

confidence: 99%

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Brouwer

Boukerroui

Oliveira

et al. 2020

Physics and Imaging in Radiation Oncology

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“…With the first commercial radiotherapy products already being used in clinical practice, the need for guidelines on commissioning and implementation is urgent. Recent recommendations for implementation and quality assurance of AI-based applications aim to support clinical teams during implementation of machine learning models in the radiotherapy workflow for contouring, planning and synthetic CT [16] .…”

Section: Discussionmentioning

confidence: 99%

Machine learning applications in radiation oncology: Current use and needs to support clinical implementation

Brouwer

Dinkla

Vandewinckele

et al. 2020

Physics and Imaging in Radiation Oncology

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Background and purpose: The use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice. Materials and methods: A survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications. Results: In total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixtynine percent (1 4 7) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice. Conclusion: The results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction.

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“…A session or pretreatment MRI (MR session ) image is acquired on the MR-Linac which is registered to the planning reference image (CT planning or MR planning ). The contours from the planning reference image are propagated to the MR session image using deformable registration or segmented using artificial intelligence contouring algorithms (113). The contours are reviewed and corrected if necessary.…”

Section: Workflow Considerations For Bladder Treatment On the Mr-linacmentioning

confidence: 99%

MR-Guided Adaptive Radiotherapy for Bladder Cancer

et al. 2021

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Radiotherapy has an important role in the curative and palliative treatment settings for bladder cancer. As a target for radiotherapy the bladder presents a number of technical challenges. These include poor tumor visualization and the variability in bladder size and position both between and during treatment delivery. Evidence favors the use of magnetic resonance imaging (MRI) as an important means of tumor visualization and local staging. The availability of hybrid systems incorporating both MRI scanning capabilities with the linear accelerator (MR-Linac) offers opportunity for in-room and real-time MRI scanning with ability of plan adaption at each fraction while the patient is on the treatment couch. This has a number of potential advantages for bladder cancer patients. In this article, we examine the technical challenges of bladder radiotherapy and explore how magnetic resonance (MR) guided radiotherapy (MRgRT) could be leveraged with the aim of improving bladder cancer patient outcomes. However, before routine clinical implementation robust evidence base to establish whether MRgRT translates into improved patient outcomes should be ascertained.

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Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Cited by 186 publications

References 135 publications

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Machine learning applications in radiation oncology: Current use and needs to support clinical implementation

MR-Guided Adaptive Radiotherapy for Bladder Cancer

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