Geometric uncertainty analysis of MLC tracking for lung SABR

Caillet, Vincent; Zwan, Benjamin; Briggs, Adam; Hardcastle, Nicholas; Szymura, Kathryn; Prodreka, Alexander; O’Brien, Ricky; Harris, Ben; Greer, Peter; Haddad, Carol; Jayamanne, Dasantha; Eade, Thomas; Booth, Jeremy; Keall, P.

doi:10.1088/1361-6560/abb0c6

Cited by 6 publications

(8 citation statements)

References 34 publications

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“…In fact, data shows that in radiation oncology, 30% of errors occur in the planning phase of therapy and 29% of errors are discovered in the treatment step of therapy [27]. This may suggest that the planning phase needs a more robust universally standardized control system and many studies have attempted to elucidate areas of improvement regarding geometric discrepancies resulting in errors [28][29][30].…”

Section: Radiation Errorsmentioning

confidence: 99%

Patient Safety: Preventing Patient Harm and Building Capacity for Patient Safety

Domer¹,

Gallagher²,

Shahabzada³

et al. 2022

Contemporary Topics in Patient Safety - Volume 1

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Patient safety is a global public health concern. It is a health care discipline with ever evolving advancement and complexity resulting in consequential rise in patient harm. Since the pandemic, patient safety has been threatened even more by laying bare the inadequacies of health systems. Many unsafe care practices, risks, and errors contribute to patient harm and overall economic burden. These include medical, diagnostic, and radiation errors, healthcare associated infections, unsafe surgical procedures and transfusion practices, sepsis, venous thromboembolism, and falls. Although patient safety has become an integral part of the healthcare delivery model and resources have been dedicated towards it, much still needs to be achieved. An attitude of inclusivity for all care teams and anyone in contact with the patient, including the patients themselves, would enhance patient safety. Incorporating this attitude from educational infancy will allow for better identification of medical errors and inculcate critical analysis of process improvement. Implementing the ‘Just Culture’ by health care organizations can build the infrastructure to eliminate avoidable harm. To reduce avoidable harm and improve safety, a constant flow of information and knowledge should be available to mitigate the risks. Lastly, proper communication and effective leadership can play an imperative role to engage stakeholders and reduce harm.

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Section: Radiation Errorsmentioning

confidence: 99%

Patient Safety: Preventing Patient Harm and Building Capacity for Patient Safety

Domer¹,

Gallagher²,

Shahabzada³

et al. 2022

Contemporary Topics in Patient Safety - Volume 1

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“…Distributions describing the combined effects of multiple sources of uncertainty can be used directly by the algorithm, even if the effects of individual sources are not known independently. Alternatively, if estimates of the effects of individual sources are known, they can be combined through simulations or mathematical operations like convolution 4,21,32 . Even uncertainties in the estimates of uncertainties can be naturally incorporated within this framework.…”

Section: Discussionmentioning

confidence: 99%

Application of an automatic, uncertainty model‐guided, target‐generating algorithm to lung stereotactic body radiotherapy

2021

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Purpose This work evaluated a new radiotherapy target‐generating framework (the αTarget algorithm) for creating internal target volumes for lung SBRT. Methods Nineteen patients previously treated with definitive intent SBRT to the lung were identified from a clinical database. For each patient's 4DCT simulation scan, deformable image registration was used between phases of the scan in order to generate voxelized models of motion for 35 individual gross tumor volumes. These motion models were then used with a new implementation of a previously described target‐generating algorithm to create new internal target volumes (αITVs). The resulting αITVs were analyzed with respect to their volume and the coverage they provided each tumor voxel per that voxel's motion model. The clinically used ITVs were similarly analyzed, and were then compared to the αITVs using paired Student's t‐tests. In addition, isotropic margins were added to the αITVs in order to determine the largest margin magnitude that could be added without exceeding the volume of the clinical ITVs. Results The αITVs increased the target coverage provided to each tumor's 5th‐percentile‐most‐covered‐voxel an average of 50.3% compared to the clinical ITVs (p < 0.0001). At the same time, the αITVs had volumes that were, on average, 31.4% smaller (p < 0.0001). The differences in volume were large enough that, on average, an extra 2 mm isotropic margin could be added to the αITV before it had a volume greater than the clinical ITV. Conclusions The αTarget algorithm can generate more effective lung SBRT internal target volumes that provide greater coverage with smaller volumes. In combination with numerous other advantages of the framework, this effectiveness makes the αTarget algorithm a powerful new method for advanced IGRT or adaptive radiotherapy techniques.

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“…Hence, it is necessary to have high geometrical accuracy for a safe clinical application of precise radiotherapy. Some recent studies describe the process of measurement and reduction in geometrical errors [5][6][7]. There are some reviews which discuss different types of errors in radiotherapy and the process to overcome these discrepancies [3,[8][9][10].…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, geometric errors have to be identified and removed for safe radiation therapy. There are many recent studies which address issues of measurement and the reduction in geometrical errors [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature

Basu

Goldsworthy²,

Gkoutos

2021

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The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors.

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Geometric uncertainty analysis of MLC tracking for lung SABR

Cited by 6 publications

References 34 publications

Patient Safety: Preventing Patient Harm and Building Capacity for Patient Safety

Patient Safety: Preventing Patient Harm and Building Capacity for Patient Safety

Application of an automatic, uncertainty model‐guided, target‐generating algorithm to lung stereotactic body radiotherapy

A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature

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