Complexity analysis of VMAT prostate plans: insights from dimensionality reduction and information theory techniques

• Autorzy: Kamperis Efstathios , Kodona Chionia , Papalexandrou Apostolia , Arsos Georgios , Heidich Anna-Bettina , Hatziioannou Konstantinos , Giannouzakos Vasileios , Papanastasiou Emmanouil

Identyfikatory

DOI

10.2478/pjmpe-2023-0015

• Języki publikacji: EN

Abstrakty

EN

Introduction: Volumetric Modulated Arc Therapy (VMAT) is a state-of-the-art prostate cancer treatment, defined by high dose gradients around targets. Its unique dose shaping incurs hidden complexity, impacting treatment deliverability, carcinogenesis, and machine strain. This study compares various aperture-based VMAT complexity indices in prostate cases using principal component and mutual information analyses. It suggests essential properties for an ideal complexity index from an information-theoretic viewpoint. Material and methods: The following ten complexity indices were calculated in 217 VMAT prostate plans: circumference over area (CoA), edge metric (EM), equivalent square field (ESF), leaf travel (LT), leaf travel modulation complexity score for VMAT (LTMCSV), mean-field area (MFA), modulation complexity score (standard MCS and VMAT variant MCSV), plan irregularity (PI), and small aperture score (SAS_5mm). Principal component analysis (PCA) was applied to explore the correlations between the metrics. The differential entropy of all metrics was also calculated, along with the mutual information for all 45 metric pairs. Results: Whole-pelvis plans had greater complexity across all indices. The first three principal components explained 96.2% of the total variance. The complexity metrics formed three groups with similar conceptual characteristics, particularly ESF, LT, MFA, PI, and EM, SAS₅. The differential entropy varied across the complexity metrics (PI having the smallest vs. EM the largest). Mutual information analysis (MIA) confirmed some metrics’ interdependence, although other pairs, such as LTMCSV/SAS_5mm, LT/MCSV, and EM/SAS_5mm, were found to share minimal MI. Conclusions: There are many complexity indices for VMAT described in the literature. PCA and MIA analyses can uncover significant overlap among them. However, this is not entirely reducible through dimensionality reduction techniques, suggesting that there also exists some reciprocity. When designing predictive models of quality assurance metrics, PCA and MIA may prove useful for feature engineering.

Słowa kluczowe

EN

prostate cancer; VMAT; plan complexity

• Wydawca: Polskie Towarzystwo Fizyki Medycznej ; De Gruyter
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2023
• Tom: Vol. 29, Iss. 3
• Strony: 143--150
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Kamperis Efstathios

• Radiation Oncology Department, Papageorgiou General Hospital, Greece

autor

Kodona Chionia

• Medical Physics Department, Papageorgiou General Hospital, Greece

autor

Papalexandrou Apostolia

• Medical Physics Department, Papageorgiou General Hospital, Greece

autor

Arsos Georgios

• Third Department of Nuclear Medicine, Papageorgiou General Hospital, Aristotle University of Thessaloniki, Greece

autor

Heidich Anna-Bettina

• Department of Hygiene, Social-Preventive Medicine & Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece

autor

Hatziioannou Konstantinos

• Medical Physics Department, Papageorgiou General Hospital, Greece

autor

Giannouzakos Vasileios

• Radiation Oncology Department, Papageorgiou General Hospital, Greece

autor

Papanastasiou Emmanouil

• Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece

Bibliografia

• 1. Sung H., Ferlay J., Siegel R.L., et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. https://doi.org/10.3322/caac.21660
• 2. Mottet N., van der Bergh R.C.N., Briers E., et al. EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2021;79(2):243-262. https://doi.org/10.1016/j.eururo.2020.09.042
• 3. Nutting C.M., Convery D.J., Cosgrove V.P., et al. Reduction of small and large bowel irradiation using an optimized intensity-modulated pelvic radiotherapy technique in patients with prostate cancer. Int J Radiat Oncol Biol Phys. 2000;48(3):649-656. https://doi.org/10.1016/s0360-3016(00)00653-2
• 4. Ren W., Sun C., Lu N., et al. Dosimetric comparison of intensity-modulated radiotherapy and volumetric-modulated arc radiotherapy in patients with prostate cancer: a meta-analysis. J Appl Clin Med Phys. 2016;17(6):254-262. https://doi.org/10.1120%2Fjacmp.v17i6.6464
• 5. Otto K., Volumetric modulated arc therapy: IMRT in a single gantry arc. Med Phys. 2008;35(1):310-317. https://doi.org/10.1118/1.2818738
• 6. Kamperis E., Kodona C., Hatziioannou K., Giannouzakos V., Complexity in Radiation Therapy: It's Complicated. Int J Radiat Oncol Biol Phys. 2020;106(1):182-184. https://doi.org/10.1016/j.ijrobp.2019.09.003
• 7. Hernandez V., Saez J., Pasler M., Jurado-Bruggeman D., Jornet N., Comparison of complexity metrics for multi-institutional evaluations of treatment plans in radiotherapy. Physics and Imaging in Radiation Oncology. 2018;5:37-43. https://doi.org/10.1016/j.phro.2018.02.002
• 8. Chiavassa S., Bessieres I., Edouard M., Mathot M., Moignier A., Complexity metrics for IMRT and VMAT plans: a review of current literature and applications. Br J Radiol. 2019;92:20190270. https://doi.org/10.1259/bjr.20190270
• 9. Alexidis P., Dragoumis D., Karatzoglou S., et al. The role of hypofractionated radiotherapy for the definitive treatment of localized prostate cancer: early results of a randomized trial. J Cancer. 2019;10(25):6217-6224. https://doi.org/10.7150%2Fjca.35510
• 10. Alexidis P., Karatzoglou S., Dragoumis D., et al. Late results of a randomized trial on the role of mild hypofractionated radiotherapy for the treatment of localized prostate cancer. J Cancer. 2020;11(5):1008-1016. https://doi.org/10.7150%2Fjca.37825
• 11. Götstedt J., Karlsson Hauer A., Bäck A., Development and evaluation of aperture-based complexity metrics using film and EPID measurements of static MLC openings. Med Phys. 2015;42(7):3911-3921. https://doi.org/10.1118/1.4921733
• 12. Younge K.C,, Matuszak M.M., Moran J.M., et al. Penalization of aperture complexity in inversely planned volumetric modulated arc therapy. Med Phys. 2021;39(11):7160-7170. https://doi.org/10.1118/1.4762566
• 13. Masi L., Doro R., Favuzza V., Cipressi S., Livi L., Impact of plan parameters on the dosimetric accuracy of volumetric modulated arc therapy. Med Phys. 2013;40(7):071718. https://doi.org/10.1118/1.4810969
• 14. Crowe S.B., Kairn T., Kenny J., et al. Treatment plan complexity metrics for predicting IMRT pre-treatment quality assurance results. Australas Phys Eng Sci Med. 2014;37(3):475-482. https://doi.org/10.1007/s13246-014-0274-9
• 15. Kairn T., Crowe S.B., Kenny J., Knight R.T., Trapp J.V., Predicting the likelihood of QA failure using treatment plan accuracy metrics. J Phys Conf Ser. 2014;489:012051. https://doi.org/10.1088/1742-6596/489/1/012051
• 16. McNiven A.L., Sharpe M.B., Purdie T.G., A new metric for assessing IMRT modulation complexity and plan deliverability. Med Phys. 2010;37(2):505-515. https://doi.org/10.1118/1.3276775
• 17. Du W, Cho SH, Zhang X, Hoffman KE, Kudchadker RJ. Quantification of beam complexity in intensity-modulated radiation therapy treatment plans. Med Phys. 2014;41(2):021716. https://doi.org/10.1118/1.4861821
• 18. Kaiser HF, Rice J. Little Jiffy, Mark IV. Educ. Psychol. Meas. 1974;34:111-117. https://doi.org/10.1177/001316447403400115
• 19. Maier MJ. Companion Package to the Book “R: Einführung durch angewandte Statistik.” https://cran.r-project.org/web/packages/REdaS/REdaS.pdf
• 20. Wolfram Research, Inc. Mathematica, Version 12.1, Champaign, IL. (2020).
• 21. Silverman BW. Density Estimation for Statistics and Data Analysis. 45 (London: Chapman & Hall/CRC., 1986).
• 22. Haykin S. Neural Networks and Learning Machines. (Pearson Education, 2010).
• 23. Kaiser HF. An index of factorial simplicity. Psychometrika. 1974;39:31-36. https://doi.org/10.1007/BF02291575
• 24. Valdes G, Scheuermann R, Hung CY, et al. A mathematical framework for virtual IMRT QA using machine learning. Med Phys. 2016;43(7):4323. https://doi.org/10.1118/1.4953835
• 25. Glenn MC, Hernandez V, Saez J, et al. Treatment plan complexity does not predict IROC Houston anthropomorphic head and neck phantom performance. Phys Med Biol. 2018;63(20):205015. https://doi.org/10.1088/1361-6560/aae29e
• 26. Chun M, Joon An H, Kwon O, et al. Impact of plan parameters and modulation indices on patient-specific QA results for standard and stereotactic VMAT. Phys Med. 2019;62:83-94. https://doi.org/10.1016/j.ejmp.2019.05.005
• 27. Santos T, Ventura T, Lopes MDC. Evaluation of the complexity of treatment plans from a national IMRT/VMAT audit - Towards a plan complexity score. Phys Med. 2020;70:75-84. https://doi.org/10.1016/j.ejmp.2020.01.015

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).