000079105 001__ 79105 000079105 005__ 20200221144224.0 000079105 0247_ $$2doi$$a10.1118/1.4964796 000079105 0248_ $$2sideral$$a97036 000079105 037__ $$aART-2016-97036 000079105 041__ $$aeng 000079105 100__ $$aGarcia-Romero, Alejandro 000079105 245__ $$aOn the new metrics for IMRT QA verification 000079105 260__ $$c2016 000079105 5060_ $$aAccess copy available to the general public$$fUnrestricted 000079105 5203_ $$aPurpose: the aim of this work is to search for new metrics that could give more reliable acceptance/ rejection criteria on the IMRT verification process and to offer solutions to the discrepancies found among different conventional metrics. Therefore, besides conventional metrics, new ones are proposed and evaluated with new tools to find correlations among them. These new metrics are based on the processing of the dosevolume histogram information, evaluating the absorbed dose differences, the dose constraint fulfillment, or modified biomathematical treatment outcome models such as tumor control probability (TCP) and normal tissue complication probability (NTCP). An additional purpose is to establish whether the new metrics yield the same acceptance/rejection plan distribution as the conventional ones. Methods: Fifty eight treatment plans concerning several patient locations are analyzed. All of them were verified prior to the treatment, using conventional metrics, and retrospectively after the treatment with the new metrics. These new metrics include the definition of three continuous functions, based on dosevolume histograms resulting from measurements evaluated with a reconstructed dose system and also with a Monte Carlo redundant calculation. The 3D gamma function for every volume of interest is also calculated. The information is also processed to obtain dTCP or dNTCP for the considered volumes of interest. These biomathematical treatment outcome models have been modified to increase their sensitivity to dose changes. A robustness index from a radiobiological point of view is defined to classify plans in robustness against dose changes. Results: Dose difference metrics can be condensed in a single parameter: the dose difference global function, with an optimal cutoff that can be determined from a receiver operating characteristics (ROC) analysis of the metric. It is not always possible to correlate differences in biomathematical treatment outcome models with dose difference metrics. This is due to the fact that the dose constraint is often far from the dose that has an actual impact on the radiobiological model, and therefore, biomathematical treatment outcome models are insensitive to big dose differences between the verification system and the treatment planning system. As an alternative, the use of modified radiobiological models which provides a better correlation is proposed. In any case, it is better to choose robust plans from a radiobiological point of view. The robustness index defined in this work is a good predictor of the plan rejection probability according to metrics derived from modified radiobiological models. The global 3D gamma-based metric calculated for each plan volume shows a good correlation with the dose difference metrics and presents a good performance in the acceptance/rejection process. Some discrepancies have been found in dose reconstruction depending on the algorithm employed. Significant and unavoidable discrepancies were found between the conventional metrics and the new ones. Conclusions: The dose difference global function and the 3D gamma for each plan volume a e good classifiers regarding dose difference metrics. ROC analysis is useful to evaluate the predictive power of the new metrics. The correlation between biomathematical treatment outcome models and the dose difference-based metrics is enhanced by using modified TCP and NTCP functions that take into account the dose constraints for each plan. The robustness index is useful to evaluate if a plan is likely to be rejected. Conventional verification should be replaced by the new metrics, which are clinically more relevant. 000079105 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/PI11-01274 000079105 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000079105 590__ $$a2.617$$b2016 000079105 591__ $$aRADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING$$b37 / 125 = 0.296$$c2016$$dQ2$$eT1 000079105 592__ $$a0.74$$b2016 000079105 593__ $$aBiophysics$$c2016$$dQ2 000079105 593__ $$aRadiology, Nuclear Medicine and Imaging$$c2016$$dQ2 000079105 593__ $$aMedicine (miscellaneous)$$c2016$$dQ2 000079105 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000079105 700__ $$0(orcid)0000-0002-4188-4151$$aHernandez-Vitoria, Araceli$$uUniversidad de Zaragoza 000079105 700__ $$0(orcid)0000-0003-3045-505X$$aMillan-Cebrian, Esther$$uUniversidad de Zaragoza 000079105 700__ $$aAlba-Escorihuela, Verónica 000079105 700__ $$0(orcid)0000-0001-7357-7546$$aSerrano-Zabaleta, Sonia 000079105 700__ $$aOrtega-Pardina, Pablo 000079105 7102_ $$11010$$2770$$aUniversidad de Zaragoza$$bDpto. Pediatría Radiol.Med.Fís$$cÁrea Radiol. y Medicina Física 000079105 773__ $$g43, 11 (2016), 6058-6071$$pMed. phys.$$tMEDICAL PHYSICS$$x0094-2405 000079105 8564_ $$s468027$$uhttps://zaguan.unizar.es/record/79105/files/texto_completo.pdf$$yPostprint 000079105 8564_ $$s122004$$uhttps://zaguan.unizar.es/record/79105/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000079105 909CO $$ooai:zaguan.unizar.es:79105$$particulos$$pdriver 000079105 951__ $$a2020-02-21-13:16:42 000079105 980__ $$aARTICLE