Sumeyra CAN, Didem KARAÇETİN

739

Assessment of Geometric Changes in ROI and its Dosimetric Consequences using Deformable Image Registration for Head and Neck Adaptive Radiation Therapy

Assessment of Geometric Changes in ROI and its Dosimetric Consequences using Deformable Image Registration for Head and Neck Adaptive Radiation Therapy

The aim of this study was to evaluate the change in volume and center of mass for a region of interest and how the changes affected cumulative dose though a Geometric Processing Unit (GPU)-based Deformable Image Registration. Ten head and neck cancer patients treated with simultaneous integrated boost on tomotherapy were retrospectively analyzed. Planning CT and 6–8 kV CT images were obtained for each case and these images were used for image registration though GPU-based dose accumulation and simulation framework to calculate accumulated dose and geometric changes for organs at risk. The cumulative dose was evaluated based on geometric changes and was compared with the planned dose. There was no statistical difference between the accumulated dose and planned dose for Dmean, V100, and V90 of PTV1 (p > 0.05). The accumulated dose was lower than the planned dose by 14.8% and 8.8% for V100 and V95 of PTV3, respectively. The cumulative dose to the medulla spinalis was higher than the planned dose by 7%; however, it was less than the planned dose by 6.6% and 4.1% for the left and right parotid glands, respectively. Weekly cumulative dose assessment is an essential quantity to determine how closely treatment planning is followed, since head and neck cancer patients undergo many anatomical changes. The GPU-based 3D image framework allows for the evaluation of real-time dose accumulation and tracking inter-fractional volume change for a region of interest. Deformable image registration is an essential tool to gain insight when adaptive radiation therapy is necessary.
Keywords:

Adaptive Radiotherapy, Deformable Image Registration, Head and Neck Cancers, IGRT Radiation Dose, Tomotherapy,

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  • 1. Atwell D, Elks J, Cahill K, Hearn N, Vignarajah D, Lagopoulos J, et al. A Review of Modern Radiation Therapy Dose Escalation in Locally Advanced Head and Neck Cancer. Clinical Oncology. 2020.
  • 2. Toledano I, Graff P, Serre A, Boisselier P, Bensadoun R-J, Ortholan C, et al. Intensity-modulated radiotherapy in head and neck cancer: results of the prospective study GORTEC 2004–03. Radiotherapy and Oncology. 2012;103(1):57-62.
  • 3. Webb S. Delivery of Intensity-Modulated Radiation Therapy Including Compensation for Organ Motion. Radiotherapy and Brachytherapy: Springer; 2009. p. 141-61.
  • 4. Peñagarícano JA, Papanikolaou N. Intensity-modulated radiotherapy for carcinoma of the head and neck. Current oncology reports. 2003;5(2):131-9.
  • 5. Sharma A, Bahl A. Intensity-modulated radiation therapy in head-and-neck carcinomas: Potential beyond sparing the parotid glands. Journal of Cancer Research and Therapeutics. 2020;16(3):425.
  • 6. Hong TS, Tomé WA, Chappell RJ, Chinnaiyan P, Mehta MP, Harari PM. The impact of daily setup variations on head-and-neck intensity-modulated radiation therapy. International Journal of Radiation Oncology* Biology* Physics. 2005;61(3):779-88.
  • 7. Suzuki M, Nishimura Y, Nakamatsu K, Okumura M, Hashiba H, Koike R, et al. Analysis of interfractional set-up errors and intrafractional organ motions during IMRT for head and neck tumors to define an appropriate planning target volume (PTV)-and planning organs at risk volume (PRV)-margins. Radiotherapy and oncology. 2006;78(3):283-90.
  • 8. O'Daniel JC, Garden AS, Schwartz DL, Wang H, Ang KK, Ahamad A, et al. Parotid gland dose in intensity-modulated radiotherapy for head and neck cancer: is what you plan what you get? International Journal of Radiation Oncology* Biology* Physics. 2007;69(4):1290-6.
  • 9. Chambers MS, Garden AS, Kies MS, Martin JW. Radiation‐induced xerostomia in patients with head and neck cancer: pathogenesis, impact on quality of life, and management. Head & Neck: Journal for the Sciences and Specialties of the Head and Neck. 2004;26(9):796-807.
  • 10. Heukelom J, Kantor ME, Mohamed AS, Elhalawani H, Kocak-Uzel E, Lin T, et al. Differences between planned and delivered dose for head and neck cancer, and their consequences for normal tissue complication probability and treatment adaptation. Radiotherapy and Oncology. 2020;142:100-6.
  • 11. Lowther NJ, Marsh SH, Louwe RJ. Dose accumulation to assess the validity of treatment plans with reduced margins in radiotherapy of head and neck cancer. Physics and Imaging in Radiation Oncology. 2020;14:53-60.
  • 12. Kanehira T, Svensson S, van Kranen S, Sonke J-J. Accurate estimation of daily delivered radiotherapy dose with an external treatment planning system. Physics and Imaging in Radiation Oncology. 2020;14:39-42.
  • 13. Greco C, Clifton Ling C. Broadening the scope of image-guided radiotherapy (IGRT). Acta Oncologica. 2008;47(7):1193-200.
  • 14. Warlick WB. Image-guided radiation therapy: techniques and strategies. Community Oncology. 2008;2(5):86-92.
  • 15. Sterzing F, Engenhart-Cabillic R, Flentje M, Debus J. Image-guided radiotherapy: a new dimension in radiation oncology. Deutsches Aerzteblatt International. 2011;108(16):274.
  • 16. Michiels S. Optimization and implementation of emerging technologies to improve radiation therapy of head-and-neck cancer. 2020.
  • 17. Fiorino C, Dell'Oca I, Pierelli A, Broggi S, De Martin E, Di Muzio N, et al. Significant improvement in normal tissue sparing and target coverage for head and neck cancer by means of helical tomotherapy. Radiotherapy and oncology. 2006;78(3):276-82.
  • 18. Loo H, Fairfoul J, Chakrabarti A, Dean J, Benson R, Jefferies S, et al. Tumour shrinkage and contour change during radiotherapy increase the dose to organs at risk but not the target volumes for head and neck cancer patients treated on the TomoTherapy HiArt™ system. Clinical Oncology. 2011;23(1):40-7.
  • 19. Farrag A, Voordeckers M, Tournel K, De Coninck P, Storme G. Pattern of failure after helical tomotherapy in head and neck cancer. Strahlentherapie und Onkologie. 2010;186(9):511-6.
  • 20. Fung NTC, Hung WM, Sze CK, Lee MCH, Ng WT. Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: Time, geometrical, and dosimetric analysis. Medical Dosimetry. 2020;45(1):60-5.
  • 21. Veiga C, McClelland J, Moinuddin S, Lourenço A, Ricketts K, Annkah J, et al. Toward adaptive radiotherapy for head and neck patients: feasibility study on using CT‐to‐CBCT deformable registration for “dose of the day” calculations. Medical physics. 2014;41(3).
  • 22. Veiga C, Lourenço AM, Mouinuddin S, van Herk M, Modat M, Ourselin S, et al. Toward adaptive radiotherapy for head and neck patients: uncertainties in dose warping due to the choice of deformable registration algorithm. Medical physics. 2015;42(2):760-9.
  • 23. Capelle L, Mackenzie M, Field C, Parliament M, Ghosh S, Scrimger R. Adaptive radiotherapy using helical tomotherapy for head and neck cancer in definitive and postoperative settings: initial results. Clinical Oncology. 2012;24(3):208-15.
  • 24. Zhang L, Wang Z, Shi C, Long T, Xu XG. The impact of robustness of deformable image registration on contour propagation and dose accumulation for head and neck adaptive radiotherapy. Journal of applied clinical medical physics. 2018;19(4):185-94.
  • 25. Scaggion A, Fiandra C, Loi G, Vecchi C, Fusella M. Free-to-use DIR solutions in radiotherapy: Benchmark against commercial platforms through a contour-propagation study. Physica Medica. 2020;74:110-7.
  • 26. Weppler S, Schinkel C, Kirkby C, Smith W. Lasso logistic regression to derive workflow-specific algorithm performance requirements as demonstrated for head and neck cancer deformable image registration in adaptive radiation therapy. Physics in Medicine & Biology. 2020;65(19):195013.
  • 27. Rigaud B, Simon A, Castelli J, Gobeli M, Ospina Arango J-D, Cazoulat G, et al. Evaluation of deformable image registration methods for dose monitoring in head and neck radiotherapy. BioMed research international. 2015;2015.
  • 28. Barber J, Yuen J, Jameson M, Schmidt L, Sykes J, Gray A, et al. Deforming to Best Practice: Key considerations for deformable image registration in radiotherapy. Journal of Medical Radiation Sciences. 2020.
  • 29. Nobnop W, Chitapanarux I, Wanwilairat S, Tharavichitkul E, Lorvidhaya V, Sripan P. Effect of deformation methods on the accuracy of deformable image registration from kilovoltage CT to tomotherapy megavoltage CT. Technology in cancer research & treatment. 2019;18:1533033818821186.
  • 30. Lowther NJ, Marsh SH, Louwe RJ. Quantifying the dose accumulation uncertainty after deformable image registration in head-and-neck radiotherapy. Radiotherapy and Oncology. 2020;143:117-25.
  • 31. Li X, Zhang Y, Shi Y, Wu S, Xiao Y, Gu X, et al. Comprehensive evaluation of ten deformable image registration algorithms for contour propagation between CT and cone-beam CT images in adaptive head & neck radiotherapy. PLoS One. 2017;12(4):e0175906.
  • 32. Rigaud B, Simon A, Castelli J, Lafond C, Acosta O, Haigron P, et al. Deformable image registration for radiation therapy: principle, methods, applications and evaluation. Acta Oncologica. 2019;58(9):1225-37.
  • 33. Fabri D, Zambrano V, Bhatia A, Furtado H, Bergmann H, Stock M, et al. A quantitative comparison of the performance of three deformable registration algorithms in radiotherapy. Zeitschrift für Medizinische Physik. 2013;23(4):279-90.
  • 34. Sharon Qi X, Neylon J, Can S, Staton R, Pukala J, Kupelian P, et al. Feasibility of Margin Reduction for Level II and III Planning Target Volume in Head-and-Neck Image-Guided Radiotherapy–Dosimetric Assessment via A Deformable Image Registration Framework. Current Cancer Therapy Reviews. 2014;10(4):323-33.
  • 35. McIntosh C, Welch M, McNiven A, Jaffray DA, Purdie TG. Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method. Physics in Medicine & Biology. 2017;62(15):5926.
  • 36. Tong N, Gou S, Yang S, Ruan D, Sheng K. Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Medical physics. 2018;45(10):4558-67.
  • 37. Guerrero T, Sanders K, Castillo E, Zhang Y, Bidaut L, Pan T, et al. Dynamic ventilation imaging from four-dimensional computed tomography. Physics in Medicine & Biology. 2006;51(4):777.
  • 38. Branchini M, Fiorino C, Dell'Oca I, Belli M, Perna L, Di Muzio N, et al. Validation of a method for “dose of the day” calculation in head-neck tomotherapy by using planning ct-to-MVCT deformable image registration. Physica Medica. 2017;39:73-9.
  • 39. Chen W, Li Y, Dyer BA, Feng X, Rao S, Benedict SH, et al. Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images. Radiation Oncology. 2020;15(1):1-10.
  • 40. Pukala J, Johnson PB, Shah AP, Langen KM, Bova FJ, Staton RJ, et al. Benchmarking of five commercial deformable image registration algorithms for head and neck patients. Journal of applied clinical medical physics. 2016;17(3):25-40.
  • 41. Elstrøm UV, Wysocka BA, Muren LP, Petersen JB, Grau C. Daily kV cone-beam CT and deformable image registration as a method for studying dosimetric consequences of anatomic changes in adaptive IMRT of head and neck cancer. Acta oncologica. 2010;49(7):1101-8.
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Assessment of Geometric Changes in ROI and its Dosimetric Consequences using Deformable Image Registration for Head and Neck Adaptive Radiation Therapy

Sumeyra CAN, Didem KARAÇETİN