Incorporating biological modeling into patient‐specific plan verification. Issue 3 (26th February 2020)

• Record Type: Journal Article
• Authors: Alexandrian, Ara N.; Mavroidis, Panayiotis; Narayanasamy, Ganesh; McConnell, Kristen A.; Kabat, Christopher N.; George, Renil B.; Defoor, Dewayne L.; Kirby, Neil; Papanikolaou, Nikos; Stathakis, Sotirios
• Is Part Of: Journal of applied clinical medical physics. Volume 21:Issue 3(2020)
• Page Start: 94
• Page End: 107
• Publication Date: 2020-02-26
• Subjects: IMRT QA -- radiobiological QA -- radiobiological verification -- radiobiology; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.12831
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Purpose: Dose–volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability calculations can provide additional insight beyond conventional dose delivery verification methods. Methods: A commercial quality assurance system was used to generate DVHs of treatment plan using the planning CT images and patient‐specific QA measurements on a phantom. Biological modeling was performed on the DVHs produced by both the treatment planning system and the quality assurance system. Results: The complication‐free tumor control probability, P +, has been calculated for previously treated intensity modulated radiotherapy (IMRT) patients with diseases in the following sites: brain (−3.9% ± 5.8%), head‐neck (+4.8% ± 8.5%), lung (+7.8% ± 1.3%), pelvis (+7.1% ± 12.1%), and prostate (+0.5% ± 3.6%). Conclusion: Dose measurements on a phantom can be used for pretreatment estimation of tumor control and normal tissue complication probabilities. Results in this study show how biological modeling can be used to provide additional insight about accuracy of delivery during pretreatment verification.