Adapting to the motion of multiple independent targets using multileaf collimator tracking for locally advanced prostate cancer: Proof of principle simulation study.
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| Title: | Adapting to the motion of multiple independent targets using multileaf collimator tracking for locally advanced prostate cancer: Proof of principle simulation study. |
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| Authors: | Hewson, Emily A.1 (AUTHOR) emily.hewson@sydney.edu.au, Ge, Yuanyuan2 (AUTHOR), O'Brien, Ricky1 (AUTHOR), Roderick, Stephanie3 (AUTHOR), Bell, Linda3 (AUTHOR), Poulsen, Per R.4 (AUTHOR), Eade, Thomas3 (AUTHOR), Booth, Jeremy T.3,5 (AUTHOR), Keall, Paul J.1 (AUTHOR), Nguyen, Doan T.1,6 (AUTHOR) |
| Source: | Medical Physics. Jan2021, Vol. 48 Issue 1, p114-124. 11p. |
| Subjects: | Tracking algorithms, Proof of concept, Exocrine glands, Prostate cancer, Small intestine, Collimators, Prostate cancer patients |
| Abstract: | Purpose: For patients with locally advanced cancer, multiple targets are treated simultaneously with radiotherapy. Differential motion between targets can compromise the treatment accuracy, yet there are currently no methods able to adapt to independent target motion. This study developed a multileaf collimator (MLC) tracking algorithm for differential motion adaptation and evaluated it in simulated treatments of locally advanced prostate cancer. Methods: A multi‐target MLC tracking algorithm was developed that consisted of three steps: (a) dividing the MLC aperture into two possibly overlapping sections assigned to the prostate and lymph nodes, (b) calculating the ideally shaped MLC aperture as a union of the individually translated sections, and (c) fitting the MLC positions to the ideal aperture shape within the physical constraints of the MLC leaves. The multi‐target tracking method was evaluated and compared with two existing motion management methods: single‐target tracking and no tracking. Treatment simulations of six locally advanced prostate cancer patients with three prostate motion traces were performed for all three motion adaptation methods. The geometric error for each motion adaptation method was calculated using the area of overexposure and underexposure of each field. The dosimetric error was estimated by calculating the dose delivered to the prostate, lymph nodes, bladder, rectum, and small bowel using a motion‐encoded dose reconstruction method. Results: Multi‐target MLC tracking showed an average improvement in geometric error of 84% compared to single‐target tracking, and 83% compared to no tracking. Multi‐target tracking maintained dose coverage to the prostate clinical target volume (CTV) D98% and planning target volume (PTV) D95% to within 4.8% and 3.9% of the planned values, compared to 1.4% and 0.7% with single‐target tracking, and 20.4% and 31.8% with no tracking. With multi‐target tracking, the node CTV D95%, PTV D90%, and gross tumor volume (GTV) D95% were within 0.3%, 0.6%, and 0.3% of the planned values, compared to 9.1%, 11.2%, and 21.1% for single‐target tracking, and 0.8%, 2.0%, and 3.2% with no tracking. The small bowel V57% was maintained within 0.2% to the plan using multi‐target tracking, compared to 8% and 3.5% for single‐target tracking and no tracking, respectively. Meanwhile, the bladder and rectum V50% increased by up to 13.6% and 5.2%, respectively, using multi‐target tracking, compared to 2.7% and 1.9% for single‐target tracking, and 11.2% and 11.5% for no tracking. Conclusions: A multi‐target tracking algorithm was developed and tracked the prostate and lymph nodes independently during simulated treatments. As the algorithm optimizes for target coverage, tracking both targets simultaneously may increase the dose delivered to the organs at risk. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 148364026 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adapting to the motion of multiple independent targets using multileaf collimator tracking for locally advanced prostate cancer: Proof of principle simulation study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hewson%2C+Emily+A%2E%22">Hewson, Emily A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> emily.hewson@sydney.edu.au</i><br /><searchLink fieldCode="AR" term="%22Ge%2C+Yuanyuan%22">Ge, Yuanyuan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22O'Brien%2C+Ricky%22">O'Brien, Ricky</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Roderick%2C+Stephanie%22">Roderick, Stephanie</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bell%2C+Linda%22">Bell, Linda</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Poulsen%2C+Per+R%2E%22">Poulsen, Per R.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Eade%2C+Thomas%22">Eade, Thomas</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Booth%2C+Jeremy+T%2E%22">Booth, Jeremy T.</searchLink><relatesTo>3,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Keall%2C+Paul+J%2E%22">Keall, Paul J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Doan+T%2E%22">Nguyen, Doan T.</searchLink><relatesTo>1,6</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Jan2021, Vol. 48 Issue 1, p114-124. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Tracking+algorithms%22">Tracking algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Proof+of+concept%22">Proof of concept</searchLink><br /><searchLink fieldCode="DE" term="%22Exocrine+glands%22">Exocrine glands</searchLink><br /><searchLink fieldCode="DE" term="%22Prostate+cancer%22">Prostate cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Small+intestine%22">Small intestine</searchLink><br /><searchLink fieldCode="DE" term="%22Collimators%22">Collimators</searchLink><br /><searchLink fieldCode="DE" term="%22Prostate+cancer+patients%22">Prostate cancer patients</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: For patients with locally advanced cancer, multiple targets are treated simultaneously with radiotherapy. Differential motion between targets can compromise the treatment accuracy, yet there are currently no methods able to adapt to independent target motion. This study developed a multileaf collimator (MLC) tracking algorithm for differential motion adaptation and evaluated it in simulated treatments of locally advanced prostate cancer. Methods: A multi‐target MLC tracking algorithm was developed that consisted of three steps: (a) dividing the MLC aperture into two possibly overlapping sections assigned to the prostate and lymph nodes, (b) calculating the ideally shaped MLC aperture as a union of the individually translated sections, and (c) fitting the MLC positions to the ideal aperture shape within the physical constraints of the MLC leaves. The multi‐target tracking method was evaluated and compared with two existing motion management methods: single‐target tracking and no tracking. Treatment simulations of six locally advanced prostate cancer patients with three prostate motion traces were performed for all three motion adaptation methods. The geometric error for each motion adaptation method was calculated using the area of overexposure and underexposure of each field. The dosimetric error was estimated by calculating the dose delivered to the prostate, lymph nodes, bladder, rectum, and small bowel using a motion‐encoded dose reconstruction method. Results: Multi‐target MLC tracking showed an average improvement in geometric error of 84% compared to single‐target tracking, and 83% compared to no tracking. Multi‐target tracking maintained dose coverage to the prostate clinical target volume (CTV) D98% and planning target volume (PTV) D95% to within 4.8% and 3.9% of the planned values, compared to 1.4% and 0.7% with single‐target tracking, and 20.4% and 31.8% with no tracking. With multi‐target tracking, the node CTV D95%, PTV D90%, and gross tumor volume (GTV) D95% were within 0.3%, 0.6%, and 0.3% of the planned values, compared to 9.1%, 11.2%, and 21.1% for single‐target tracking, and 0.8%, 2.0%, and 3.2% with no tracking. The small bowel V57% was maintained within 0.2% to the plan using multi‐target tracking, compared to 8% and 3.5% for single‐target tracking and no tracking, respectively. Meanwhile, the bladder and rectum V50% increased by up to 13.6% and 5.2%, respectively, using multi‐target tracking, compared to 2.7% and 1.9% for single‐target tracking, and 11.2% and 11.5% for no tracking. Conclusions: A multi‐target tracking algorithm was developed and tracked the prostate and lymph nodes independently during simulated treatments. As the algorithm optimizes for target coverage, tracking both targets simultaneously may increase the dose delivered to the organs at risk. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.14572 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 114 Subjects: – SubjectFull: Tracking algorithms Type: general – SubjectFull: Proof of concept Type: general – SubjectFull: Exocrine glands Type: general – SubjectFull: Prostate cancer Type: general – SubjectFull: Small intestine Type: general – SubjectFull: Collimators Type: general – SubjectFull: Prostate cancer patients Type: general Titles: – TitleFull: Adapting to the motion of multiple independent targets using multileaf collimator tracking for locally advanced prostate cancer: Proof of principle simulation study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hewson, Emily A. – PersonEntity: Name: NameFull: Ge, Yuanyuan – PersonEntity: Name: NameFull: O'Brien, Ricky – PersonEntity: Name: NameFull: Roderick, Stephanie – PersonEntity: Name: NameFull: Bell, Linda – PersonEntity: Name: NameFull: Poulsen, Per R. – PersonEntity: Name: NameFull: Eade, Thomas – PersonEntity: Name: NameFull: Booth, Jeremy T. – PersonEntity: Name: NameFull: Keall, Paul J. – PersonEntity: Name: NameFull: Nguyen, Doan T. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 48 – Type: issue Value: 1 Titles: – TitleFull: Medical Physics Type: main |
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