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Towards Knowledge Based Planning for CyberKnife®Stereotactic Radiosurgery Treatments of Multiple Intracranial Metastases.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Towards Knowledge Based Planning for CyberKnife®Stereotactic Radiosurgery Treatments of Multiple Intracranial Metastases./
Author:	Chalkley, Adam B.
Description:	1 online resource (175 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: A.
Contained By:	Dissertations Abstracts International85-11A.
Subject:	Prescriptions. -
Online resource:	click for full text (PQDT)
ISBN:	9798382634531

Towards Knowledge Based Planning for CyberKnife®Stereotactic Radiosurgery Treatments of Multiple Intracranial Metastases.
Chalkley, Adam B.

Towards Knowledge Based Planning for CyberKnife®Stereotactic Radiosurgery Treatments of Multiple Intracranial Metastases. - 1 online resource (175 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: A.

Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2023.

Includes bibliographical references

Background:Stereotactic Radiosurgery (SRS) is used to treat both solitary and multiple Brain Metastases (BM). Improvements in treatment efficacy for primary tumours increases BM incidence, placing greater demands on centres delivering SRS. Knowledge Based Planning (KBP) has been demonstrated as a method to reduce the time required for radiotherapy planning, and so may help elevate the additional burden on SRS services.Methods: Planning data from a dedicated SRS platform (Accuray Cyber: KnifeA®) audit database was used to train and assess a series of KBP models, predicting the volume of whole brain (excluding Planning Target Volume; PTV) receiving radionecrosis tolerance doses. Models were taken from literature, including Bohoudi et al. (2016), Cummins et al. (2020) and Yu et al. (2021). Additional models based on a power law dose fall-off from the lesion centre were also explored, including a power law shifted away from the PTV centre. 289 single and 381 multi-lesion cases were used for training, with 122 and 259 independent cases for testing. Assessment included 10-fold cross validation on the training data, for a range of error metrics such as the Mean Absolute Percentage Error (MAPE). Model parameters learnt on single lesion cases were also applied to multiple BM cases; and finally voxelised variants of the power law models were created to assess the interaction between lesions.Results: For single lesions the MAPE from the training data cross validation (mean A± standard deviation) was 29 A± 6.9%, 23 A± 5.3%, 23 A± 6.0% and 22 A± 5.9% for Bohoudi et al. (2016), Cummins et al. (2020), Yu et al. (2021) and the shifted power law respectively; with errors of 37%, 22%, 21% and 20% for the test data. For multiple lesions, the MAPE was slightly lower at 20 A± 3.5%, 20 A± 3.8% and 20 A± 3.7% for Bohoudi et al. (2016), Yu et al. (2021) and the shifted power law; and 24%, 23% and 23% for the test data. The voxelised shifted power law model for multiple lesions had a MAPEof 18 A± 2.4% and 19% on the training and test data respectively. For some methods large errors occurred when applying single lesion parameters to multiple BM cases; for Bohoudi et al. (2016) the median MAPE [95% Confidence Interval] was 38 [32, 45]% for single lesion model coefficients compared to 23 [19, 26]% for multiple lesion learnt parameters.Conclusions:Separate models are required for both single and multiple lesion cases for Cyber: Knife treatments. The improvement derived from including lesion separation for these simple models is not sufficient to warrant the extra time required to acquire the information. Power law dose fall-off models were equivocal with other methods for Cyber: Knife, adding to existing evidence for this type of relationship, but further investigation is required.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382634531Subjects--Topical Terms:

1472769
Prescriptions.
Index Terms--Genre/Form:

554714
Electronic books.

Towards Knowledge Based Planning for CyberKnife®Stereotactic Radiosurgery Treatments of Multiple Intracranial Metastases.
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100 1 $a Chalkley, Adam B. $3 1472768
245 1 0 $a Towards Knowledge Based Planning for CyberKnife®Stereotactic Radiosurgery Treatments of Multiple Intracranial Metastases.
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300 $a 1 online resource (175 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: A.
500 $a Advisor: Hayes, Geoff.
502 $a Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2023.
504 $a Includes bibliographical references
520 $a Background:Stereotactic Radiosurgery (SRS) is used to treat both solitary and multiple Brain Metastases (BM). Improvements in treatment efficacy for primary tumours increases BM incidence, placing greater demands on centres delivering SRS. Knowledge Based Planning (KBP) has been demonstrated as a method to reduce the time required for radiotherapy planning, and so may help elevate the additional burden on SRS services.Methods: Planning data from a dedicated SRS platform (Accuray Cyber: KnifeA®) audit database was used to train and assess a series of KBP models, predicting the volume of whole brain (excluding Planning Target Volume; PTV) receiving radionecrosis tolerance doses. Models were taken from literature, including Bohoudi et al. (2016), Cummins et al. (2020) and Yu et al. (2021). Additional models based on a power law dose fall-off from the lesion centre were also explored, including a power law shifted away from the PTV centre. 289 single and 381 multi-lesion cases were used for training, with 122 and 259 independent cases for testing. Assessment included 10-fold cross validation on the training data, for a range of error metrics such as the Mean Absolute Percentage Error (MAPE). Model parameters learnt on single lesion cases were also applied to multiple BM cases; and finally voxelised variants of the power law models were created to assess the interaction between lesions.Results: For single lesions the MAPE from the training data cross validation (mean A± standard deviation) was 29 A± 6.9%, 23 A± 5.3%, 23 A± 6.0% and 22 A± 5.9% for Bohoudi et al. (2016), Cummins et al. (2020), Yu et al. (2021) and the shifted power law respectively; with errors of 37%, 22%, 21% and 20% for the test data. For multiple lesions, the MAPE was slightly lower at 20 A± 3.5%, 20 A± 3.8% and 20 A± 3.7% for Bohoudi et al. (2016), Yu et al. (2021) and the shifted power law; and 24%, 23% and 23% for the test data. The voxelised shifted power law model for multiple lesions had a MAPEof 18 A± 2.4% and 19% on the training and test data respectively. For some methods large errors occurred when applying single lesion parameters to multiple BM cases; for Bohoudi et al. (2016) the median MAPE [95% Confidence Interval] was 38 [32, 45]% for single lesion model coefficients compared to 23 [19, 26]% for multiple lesion learnt parameters.Conclusions:Separate models are required for both single and multiple lesion cases for Cyber: Knife treatments. The improvement derived from including lesion separation for these simple models is not sufficient to warrant the extra time required to acquire the information. Power law dose fall-off models were equivocal with other methods for Cyber: Knife, adding to existing evidence for this type of relationship, but further investigation is required.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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650 4 $a Metastasis. $3 673770
650 4 $a Radiosurgery. $3 769208
650 4 $a Planning. $3 660157
650 4 $a Neural networks. $3 1011215
650 4 $a Conformity. $3 1106079
650 4 $a Dosimetry. $3 1472754
650 4 $a Radiation therapy. $3 1471055
650 4 $a Nuclear physics. $3 591618
650 4 $a Oncology. $3 593951
650 4 $a Therapy. $3 1181514
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710 2 $a The University of Manchester (United Kingdom). $3 1213791
773 0 $t Dissertations Abstracts International $g 85-11A.
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