000298940 001__ 298940
000298940 005__ 20250219163804.0
000298940 0247_ $$2doi$$a10.3390/cancers17030504
000298940 0247_ $$2pmid$$apmid:39941871
000298940 0247_ $$2pmc$$apmc:PMC11816179
000298940 037__ $$aDKFZ-2025-00374
000298940 041__ $$aEnglish
000298940 082__ $$a610
000298940 1001_ $$00000-0002-7433-0665$$aSevilla-Moreno, Andrés Camilo$$b0
000298940 245__ $$aInterval Analysis-Based Optimization: A Robust Model for Intensity-Modulated Radiotherapy (IMRT).
000298940 260__ $$aBasel$$bMDPI$$c2025
000298940 3367_ $$2DRIVER$$aarticle
000298940 3367_ $$2DataCite$$aOutput Types/Journal article
000298940 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1739950305_4499
000298940 3367_ $$2BibTeX$$aARTICLE
000298940 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000298940 3367_ $$00$$2EndNote$$aJournal Article
000298940 500__ $$aDivision of Medical Physics in Radiation Oncology, 
000298940 520__ $$aBackground: Cancer remains one of the leading causes of mortality worldwide, with radiotherapy playing a crucial role in its treatment. Intensity-modulated radiotherapy (IMRT) enables precise dose delivery to tumors while sparing healthy tissues. However, geometric uncertainties such as patient positioning errors and anatomical deformations can compromise treatment accuracy. Traditional methods use safety margins, which may lead to excessive irradiation of healthy organs or insufficient tumor coverage. Robust optimization techniques, such as minimax approaches, attempt to address these uncertainties but can result in overly conservative treatment plans. This study introduces an interval analysis-based optimization model for IMRT, offering a more flexible approach to uncertainty management. Methods: The proposed model represents geometric uncertainties using interval dose influence matrices and incorporates Bertoluzza's metric to balance tumor coverage and organ-at-risk (OAR) protection. The θ parameter allows controlled robustness modulation. The model was implemented in matRad, an open-source treatment planning system, and evaluated on five prostate cancer cases. Results were compared against traditional Planning Target Volume (PTV) and minimax robust optimization approaches. Results: The interval-based model improved tumor coverage by 5.8% while reducing bladder dose by 4.2% compared to PTV. In contrast, minimax robust optimization improved tumor coverage by 25.8% but increased bladder dose by 23.2%. The interval-based approach provided a better balance between tumor coverage and OAR protection, demonstrating its potential to enhance treatment effectiveness without excessive conservatism. Conclusions: This study presents a novel framework for IMRT planning that improves uncertainty management through interval analysis. By allowing adjustable robustness modulation, the proposed model enables more personalized and clinically adaptable treatment plans. These findings highlight the potential of interval analysis as a powerful tool for optimizing radiotherapy outcomes, balancing treatment efficacy and patient safety.
000298940 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
000298940 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
000298940 650_7 $$2Other$$aIMRT
000298940 650_7 $$2Other$$ainterval analysis
000298940 650_7 $$2Other$$aradiotherapy
000298940 650_7 $$2Other$$arobust optimization
000298940 650_7 $$2Other$$auncertainty
000298940 7001_ $$aPuerta-Yepes, María Eugenia$$b1
000298940 7001_ $$0P:(DE-He78)dfd5aaf608015baaaed0a15b473f1336$$aWahl, Niklas$$b2$$udkfz
000298940 7001_ $$00000-0001-9866-0864$$aBenito-Herce, Rafael$$b3
000298940 7001_ $$aCabal-Arango, Gonzalo$$b4
000298940 773__ $$0PERI:(DE-600)2527080-1$$a10.3390/cancers17030504$$gVol. 17, no. 3, p. 504 -$$n3$$p504$$tCancers$$v17$$x2072-6694$$y2025
000298940 909CO $$ooai:inrepo02.dkfz.de:298940$$pVDB
000298940 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)dfd5aaf608015baaaed0a15b473f1336$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000298940 9131_ $$0G:(DE-HGF)POF4-315$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vBildgebung und Radioonkologie$$x0
000298940 9141_ $$y2025
000298940 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bCANCERS : 2022$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-14
000298940 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bCANCERS : 2022$$d2024-12-14
000298940 9201_ $$0I:(DE-He78)E041-20160331$$kE041$$lMed. Physik in der Radioonkologie$$x0
000298940 980__ $$ajournal
000298940 980__ $$aVDB
000298940 980__ $$aI:(DE-He78)E041-20160331
000298940 980__ $$aUNRESTRICTED