Predictive performance of an OVH-based treatment planning quality assurance model for prostate VMAT: Assessing dependence on training cohort size and composition.
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| Title: | Predictive performance of an OVH-based treatment planning quality assurance model for prostate VMAT: Assessing dependence on training cohort size and composition. |
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| Authors: | Burton, Alex1,2 (AUTHOR) alex.burton@petermac.org, Norvill, Craig3 (AUTHOR), Ebert, Martin A1,4 (AUTHOR) |
| Source: | Medical Dosimetry. Winter2019, Vol. 44 Issue 4, p315-323. 9p. |
| Subjects: | Radiotherapy treatment planning, Prostate cancer, Prostate cancer patients, Quality assurance, Rectum, Prostate, Volumetric-modulated arc therapy, Histograms |
| Abstract: | Radiotherapy treatment planning quality assurance models are used to assess overall plan quality in terms of dose-volume characteristics, by predicting an optimal dosimetry based on a dataset of prior cases (the training cohort). In this study, a treatment planning quality assurance model for prostate cancer patients treated with volumetric modulated arc therapy was developed using the concept of the overlap volume histogram for geometric comparison to the training cohort. The model was developed on the publically available Erasmus iCycle dataset in order to remove the effect of plan quality/inter-planner variability on the model's predictive capabilities. The model was used to predict anus, rectum, and bladder dose volume histograms. Two versions were developed: the n = 114 case (leave-one-out method) which made predictions using the complete Erasmus dataset, and the similarity index (SI)-based model which used a smaller training cohort allocated in order of geometric similarity determined using an overlap volume histogram-derived SI. The difference in mean dose (predicted-achieved) of the SI model at cohort sizes of 10, 20, 30, 40, 50, 75, and 100 was compared to the leave-one-out method for 5 patients, in an attempt to determine the "optimum" cohort size for the SI-based model in this dataset. Performance of the optimized SI model was compared to the leave-one-out method for all patients using the following metrics: difference in mean and median dose, difference in V65Gy and V75Gy (rectum only), similarity of predicted and achieved mean dose, and mean dose volume histograms residual. The "optimum" cohort size for the SI-based model was determined to be 45. The SI-based model implementing this cohort size yielded slightly better outcomes in all performance metrics for the rectum and anus, but worse for the bladder. SI-based training cohort allocation can lead to better predictive efficacy, but the cohort size should be optimized for each individual organ. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Dosimetry is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 139326932 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predictive performance of an OVH-based treatment planning quality assurance model for prostate VMAT: Assessing dependence on training cohort size and composition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Burton%2C+Alex%22">Burton, Alex</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> alex.burton@petermac.org</i><br /><searchLink fieldCode="AR" term="%22Norvill%2C+Craig%22">Norvill, Craig</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ebert%2C+Martin+A%22">Ebert, Martin A</searchLink><relatesTo>1,4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Dosimetry%22">Medical Dosimetry</searchLink>. Winter2019, Vol. 44 Issue 4, p315-323. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Radiotherapy+treatment+planning%22">Radiotherapy treatment planning</searchLink><br /><searchLink fieldCode="DE" term="%22Prostate+cancer%22">Prostate cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Prostate+cancer+patients%22">Prostate cancer patients</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+assurance%22">Quality assurance</searchLink><br /><searchLink fieldCode="DE" term="%22Rectum%22">Rectum</searchLink><br /><searchLink fieldCode="DE" term="%22Prostate%22">Prostate</searchLink><br /><searchLink fieldCode="DE" term="%22Volumetric-modulated+arc+therapy%22">Volumetric-modulated arc therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Histograms%22">Histograms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Radiotherapy treatment planning quality assurance models are used to assess overall plan quality in terms of dose-volume characteristics, by predicting an optimal dosimetry based on a dataset of prior cases (the training cohort). In this study, a treatment planning quality assurance model for prostate cancer patients treated with volumetric modulated arc therapy was developed using the concept of the overlap volume histogram for geometric comparison to the training cohort. The model was developed on the publically available Erasmus iCycle dataset in order to remove the effect of plan quality/inter-planner variability on the model's predictive capabilities. The model was used to predict anus, rectum, and bladder dose volume histograms. Two versions were developed: the n = 114 case (leave-one-out method) which made predictions using the complete Erasmus dataset, and the similarity index (SI)-based model which used a smaller training cohort allocated in order of geometric similarity determined using an overlap volume histogram-derived SI. The difference in mean dose (predicted-achieved) of the SI model at cohort sizes of 10, 20, 30, 40, 50, 75, and 100 was compared to the leave-one-out method for 5 patients, in an attempt to determine the "optimum" cohort size for the SI-based model in this dataset. Performance of the optimized SI model was compared to the leave-one-out method for all patients using the following metrics: difference in mean and median dose, difference in V65Gy and V75Gy (rectum only), similarity of predicted and achieved mean dose, and mean dose volume histograms residual. The "optimum" cohort size for the SI-based model was determined to be 45. The SI-based model implementing this cohort size yielded slightly better outcomes in all performance metrics for the rectum and anus, but worse for the bladder. SI-based training cohort allocation can lead to better predictive efficacy, but the cohort size should be optimized for each individual organ. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Dosimetry is the property of Elsevier B.V. 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: 10.1016/j.meddos.2018.11.003 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 315 Subjects: – SubjectFull: Radiotherapy treatment planning Type: general – SubjectFull: Prostate cancer Type: general – SubjectFull: Prostate cancer patients Type: general – SubjectFull: Quality assurance Type: general – SubjectFull: Rectum Type: general – SubjectFull: Prostate Type: general – SubjectFull: Volumetric-modulated arc therapy Type: general – SubjectFull: Histograms Type: general Titles: – TitleFull: Predictive performance of an OVH-based treatment planning quality assurance model for prostate VMAT: Assessing dependence on training cohort size and composition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Burton, Alex – PersonEntity: Name: NameFull: Norvill, Craig – PersonEntity: Name: NameFull: Ebert, Martin A IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Winter2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 09583947 Numbering: – Type: volume Value: 44 – Type: issue Value: 4 Titles: – TitleFull: Medical Dosimetry Type: main |
| ResultId | 1 |
