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dc.contributor.authorZhang, Pengfei*
dc.contributor.authorFan, Neng*
dc.contributor.authorShan, Jie*
dc.contributor.authorSchild, Steven E.*
dc.contributor.authorBues, Martin*
dc.contributor.authorLiu, Wei*
dc.date.accessioned2017-12-04T23:44:01Z
dc.date.available2017-12-04T23:44:01Z
dc.date.issued2017-09
dc.identifier.citationMixed integer programming with dose-volume constraints in intensity-modulated proton therapy 2017, 18 (5):29 Journal of Applied Clinical Medical Physicsen
dc.identifier.issn15269914
dc.identifier.pmid28681976
dc.identifier.doi10.1002/acm2.12130
dc.identifier.urihttp://hdl.handle.net/10150/626184
dc.description.abstractBackground: In treatment planning for intensity-modulated proton therapy (IMPT), we aim to deliver the prescribed dose to the target yet minimize the dose to adjacent healthy tissue. Mixed-integer programming (MIP) has been applied in radiation therapy to generate treatment plans. However, MIP has not been used effectively for IMPT treatment planning with dose-volume constraints. In this study, we incorporated dose-volume constraints in an MIP model to generate treatment plans for IMPT. Methods: We created a new MIP model for IMPT with dose volume constraints. Two groups of IMPT treatment plans were generated for each of three patients by using MIP models for a total of six plans: one plan was derived with the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method while the other plan was derived with our MIP model with dose-volume constraints. We then compared these two plans by dose-volume histogram (DVH) indices to evaluate the performance of the new MIP model with dose-volume constraints. In addition, we developed a model to more efficiently find the best balance between tumor coverage and normal tissue protection. Results: The MIP model with dose-volume constraints generates IMPT treatment plans with comparable target dose coverage, target dose homogeneity, and the maximum dose to organs at risk (OARs) compared to treatment plans from the conventional quadratic programming method without any tedious trial-and-error process. Some notable reduction in the mean doses of OARs is observed. Conclusions: The treatment plans from our MIP model with dose-volume constraints can meetall dose-volume constraints for OARs and targets without any tedious trial-and-error process. This model has the potential to automatically generate IMPT plans with consistent plan quality among different treatment planners and across institutions and better protection for important parallel OARs in an effective way.
dc.description.sponsorshipNational Cancer Institute (NCI) [K25CA168984]en
dc.language.isoenen
dc.publisherWILEYen
dc.relation.urlhttp://doi.wiley.com/10.1002/acm2.12130en
dc.rights© 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the Creative Commons Attribution License.en
dc.subjectdose-volume constraintsen
dc.subjectintensity-modulated proton therapyen
dc.subjectmixed-integer programmingen
dc.titleMixed integer programming with dose-volume constraints in intensity-modulated proton therapyen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Syst & Ind Engnen
dc.identifier.journalJournal of Applied Clinical Medical Physicsen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionDepartment of Radiation Oncology; Mayo Clinic; Scottsdale AZ USA
dc.contributor.institutionDepartment of Systems & Industrial Engineering; University of Arizona; Tucson AZ USA
dc.contributor.institutionDepartment of Biomedical Informatics; Arizona State University; Phoenix AZ USA
dc.contributor.institutionDepartment of Radiation Oncology; Mayo Clinic; Scottsdale AZ USA
dc.contributor.institutionDepartment of Radiation Oncology; Mayo Clinic; Scottsdale AZ USA
dc.contributor.institutionDepartment of Radiation Oncology; Mayo Clinic; Scottsdale AZ USA
refterms.dateFOA2018-09-12T00:20:53Z
html.description.abstractBackground: In treatment planning for intensity-modulated proton therapy (IMPT), we aim to deliver the prescribed dose to the target yet minimize the dose to adjacent healthy tissue. Mixed-integer programming (MIP) has been applied in radiation therapy to generate treatment plans. However, MIP has not been used effectively for IMPT treatment planning with dose-volume constraints. In this study, we incorporated dose-volume constraints in an MIP model to generate treatment plans for IMPT. Methods: We created a new MIP model for IMPT with dose volume constraints. Two groups of IMPT treatment plans were generated for each of three patients by using MIP models for a total of six plans: one plan was derived with the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method while the other plan was derived with our MIP model with dose-volume constraints. We then compared these two plans by dose-volume histogram (DVH) indices to evaluate the performance of the new MIP model with dose-volume constraints. In addition, we developed a model to more efficiently find the best balance between tumor coverage and normal tissue protection. Results: The MIP model with dose-volume constraints generates IMPT treatment plans with comparable target dose coverage, target dose homogeneity, and the maximum dose to organs at risk (OARs) compared to treatment plans from the conventional quadratic programming method without any tedious trial-and-error process. Some notable reduction in the mean doses of OARs is observed. Conclusions: The treatment plans from our MIP model with dose-volume constraints can meetall dose-volume constraints for OARs and targets without any tedious trial-and-error process. This model has the potential to automatically generate IMPT plans with consistent plan quality among different treatment planners and across institutions and better protection for important parallel OARs in an effective way.


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