Advertisement

A density assignment method for dose monitoring in head-and-neck radiotherapy

  • A. Barateau
  • N. Perichon
  • J. Castelli
  • U. Schick
  • O. Henry
  • E. Chajon
  • A. Simon
  • C. Lafond
  • R. De Crevoisier
Original Article
  • 36 Downloads

Abstract

Background and purpose

During head-and-neck (H&N) radiotherapy, the parotid glands (PGs) may be overdosed; thus, a tool is required to monitor the delivered dose. This study aimed to assess the dose accuracy of a patient-specific density assignment method (DAM) for dose calculation to monitor the dose to PGs during treatment.

Patients and methods

Forty patients with H&N cancer received an intensity modulated radiation therapy (IMRT), among whom 15 had weekly CTs. Dose distributions were calculated either on the CTs (CTref), on one-class CTs (1C-CT, water), or on three-class CTs (3C-CT, water-air-bone). The inter- and intra-patient DAM uncertainties were evaluated by the difference between doses calculated on CTref and 1C-CTs or 3C-CTs. PG mean dose (Dmean) and spinal cord maximum dose (D2%) were considered. The cumulated dose to the PGs was estimated by the mean Dmean of the weekly CTs.

Results

The mean (maximum) inter-patient DAM dose uncertainties for the PGs (in cGy) were 23 (75) using 1C-CTs and 12 (50) using 3C-CTs (p ≤ 0.001). For the spinal cord, these uncertainties were 118 (245) and 15 (67; p ≤ 0.001). The mean (maximum) DAM dose uncertainty between cumulated doses calculated on CTs and 3C-CTs was 7 cGy (45 cGy) for the PGs. Considering the difference between the planned and cumulated doses, 53% of the ipsilateral and 80% of the contralateral PGs were overdosed by +3.6 Gy (up to 8.2 Gy) and +1.9 Gy (up to 5.2 Gy), respectively.

Conclusion

The uncertainty of the three-class DAM appears to be clinically non-significant (<0.5 Gy) compared with the PG overdose (up to 8.2 Gy). This DAM could therefore be used to monitor PG doses and trigger replanning.

Keywords

Parotid gland monitoring Dose calculation Density assignment Dose-guided radiotherapy Head-and-neck cancer 

Dichtezuordnungsmethode zur Dosisüberwachung bei der Strahlentherapie von Kopf-Hals-Tumoren

Zusammenfassung

Zielsetzung

Die Strahlentherapie bei Kopf-Hals-Tumoren (H&N) kann zu einer Dosisbelastung der Speicheldrüsen führen. Daher ist eine Methode erforderlich, um die Parotisdosis zu kontrollieren. Ziel der Studie war es, die Genauigkeit der Dosisberechnung einer patientenspezifischen auf Dichtezuweisung basierenden Methode (DAM) zu erfassen, um die Parotisdosis während der Bestrahlung zu überwachen.

Methoden

Es wurden 40 Patienten mit H&N-Tumoren mit intensitätsmodulierter Strahlentherapie (IMRT) behandelt, von denen 15 eine wöchentliche Computertomographie (CT) durchliefen. Die Dosisverteilung wurde mittels CT (CTref), Ein-Klasse-CT (1C-CT, Wasser) oder Drei-Klassen-CT (3C-CT, Wasser-Luft-Knochen) kalkuliert. Ungenauigkeiten der Inter- und Intra-Patient-DAM wurden anhand des Unterschieds zwischen der auf der CTref und der auf der 1C-CT oder auf der 3C-CT kalkulierten Dosis evaluiert. Die mittlere Parotisdosis (Dmean) und die maximale dosis (D2%) des Rückenmarks wurden berücksichtigt. Die kumulierte Parotisdosis wurde anhand des mittleren Dmean-Werts von der wöchentlichen CT geschätzt.

Ergebnisse

Die mittlere (maximale) Inter-Patient-DAM-Dosisungenauigkeit für die Parotis (in cGy) lagen bei 23 (75) mit der 1C-CT und bei 12 (50) mit der 3C-CT (p ≤ 0,001). Für das Rückenmark lagen diese Ungenauigkeiten bei 118 (245) und 15 (67; p ≤ 0,001). Für die Parotis lag die mittlere (maximale) DAM-Dosisunsicherheit zwischen der auf CT und der auf der 3C-CT kalkulierten Dosis bei 7 cGy (45 cGy). Wurde der Unterschied zwischen der geplanten und der kumulierten Dosis berücksichtigt, zeigte sich eine Überdosierung von 53% der ipsilateralen und 80% der kontralateralen Parotis von +3,6 Gy (bis zu 8,2 Gy) bzw. +1,9 Gy (bis zu 5,2 Gy).

Schlussfolgerung

Die Anwendung der 3‑Klassen-DAM ergab keinen klinisch signifikanten Unterschied in der Parotisdosis (<0,5 Gy) verglichen mit der gesamten Parotisüberdosierung (bis zu 8,2 Gy). Daher könnte diese DAM angewandt werden, um die Dosis zu kontrollieren sowie die Replanung zu steuern.

Schlüsselwörter

Dosisüberwachung Dichtezuordnung Kopf-Hals-Tumore 

Notes

Conflict of interest

A. Barateau, N. Perichon, J. Castelli, U. Schick, O. Henry, E. Chajon, A. Simon, C. Lafond and, R. De Crevoisier declare that they have no competing interests.

Supplementary material

66_2018_1379_MOESM1_ESM.tif (1.5 mb)
Fig. 1. Inter-patient study workflow for dose calculation based on reference planning CTs, one-class CTs (1C-CTs), and three-class CTs (3C-CTs). CP control point; MU monitor unit; B beam parameters
66_2018_1379_MOESM2_ESM.tif (1.3 mb)
Fig. 2. Relative intra-patient dose differences between the three-class density assignment method (DAM) and reference doses for the ipsilateral and contralateral parotid glands (2a and 2b) and the spinal cord (c). The relative dose differences were calculated for each of the 15 patients on planning CT0 and the five weekly CTs. The vertical error bar corresponds to the standard deviation (SD). Positive values indicate an overdose of the three-class DAM compared to the reference. Negative values indicate an underdose of the DAM. The mean (±SD) Dmean were 35.0 Gy (±9.3) for the ipsilateral PGs and 28.1 Gy (±5.0) for the contralateral PGs. The mean D2% (±SD) was 42.0 Gy (±4.4) for the spinal cord
66_2018_1379_MOESM3_ESM.tif (1.9 mb)
Fig. 3. Parotid gland (PG) volume shrinkage (in percentage, Fig. 3a and in absolute volume, Fig. 3b) according to parotid gland dose monitoring [difference between cumulated dose on CTs and planned dose on CT]. The parotid gland volume shrinkage was estimated as the difference between the volume on the last CT of the treatment and the volume on planning
66_2018_1379_MOESM4_ESM.tif (2 mb)
Fig. 4. Tumor gland volume shrinkage (in percentage, Fig. 4a and in terms of absolute volume, Fig. 4b) according to parotid gland dose monitoring [difference between cumulated dose on CTs and planned dose on CT]. The tumor (CTV70) volume shrinkage was estimated as the difference between the volume on the last CT of the treatment and the volume on planning

References

  1. 1.
    Barker JL, Garden AS, Ang KK, O’Danierl JC, Wang H, Court LE (2004) Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system. Int J Radiat Oncol Biol Phys 59:960–970CrossRefGoogle Scholar
  2. 2.
    Nishi T, Nishimura Y, Shibata T, Tamura M, Nishigaito N, Okumura M (2013) Volume and dosimetric changes and initial clinical experience of a two-step adaptive intensity modulated radiation therapy (IMRT) scheme for head and neck cancer. Radiother Oncol 106:85–89CrossRefGoogle Scholar
  3. 3.
    Marzi S, Pinnaro P, D’Alessio D, Strigari L, Bruzzaniti V, Giordano C (2012) Anatomical and dose changes of gross tumour volume and parotid glands for head and neck cancer patients during intensity-modulated radiotherapy: effect on the probability of xerostomia incidence. Clin Oncol 24:e54–e62CrossRefGoogle Scholar
  4. 4.
    Castelli J, Simon A, Rigaud B, Lafond C, Chajon E, Ospina JD (2016) A nomogram to predict parotid gland overdose in head and neck IMRT. Radiat Oncol.  https://doi.org/10.1186/s13014-016-0650-6 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Duma MN, Kampfer S, Schuster T, Winkler C, Geinitz H (2012) Adaptive radiotherapy for soft tissue changes during helical tomotherapy for head and neck cancer. Strahlenther Onkol 188:243–247CrossRefGoogle Scholar
  6. 6.
    Brouwer CL, Steenbakkers RJHM, Langendijk JA, Sijtsema NM (2015) Identifying patients who may benefit from adaptive radiotherapy: Does the literature on anatomic and dosimetric changes in head and neck organs at risk during radiotherapy provide information to help? Radiother Oncol 115:285–294CrossRefGoogle Scholar
  7. 7.
    Zhang P, Simon A, Rigaud B, Castelli J, Ospina Arango JD, Nassef M (2016) Optimal adaptive IMRT strategy to spare the parotid glands in oropharyngeal cancer. Radiother Oncol 120:41–47CrossRefGoogle Scholar
  8. 8.
    Veiga C, McClelland J, Moinuddin S, Lourenço A, Ricketts K, Annkah J (2014) Toward adaptive radiotherapy for head and neck patients: feasibility study on using CT-to-CBCT deformable registration for ‘dose of the day’ calculations. Med Phys.  https://doi.org/10.1118/1.4864240 CrossRefPubMedGoogle Scholar
  9. 9.
    Elstrøm UV, Muren LP, Petersen JBB, Grau C (2011) Evaluation of image quality for different kV cone-beam CT acquisition and reconstruction methods in the head and neck region. Acta Oncol 50:908–917CrossRefGoogle Scholar
  10. 10.
    Gardner SJ, Studenski MT, Giaddui T, Cui Y, Galvin J, Yu Y (2014) Investigation into image quality and dose for different patient geometries with multiple cone-beam CT systems. Med Phys.  https://doi.org/10.1118/1.4865788 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Fotina I, Hopfgartner J, Stock M, Steininger T, Lütgendorf-Caucig C, Georg D (2012) Feasibility of CBCT-based dose calculation: comparative analysis of HU adjustment techniques. Radiother Oncol 104:249–256CrossRefGoogle Scholar
  12. 12.
    Dunlop A, McQuaid D, Nill S, Murray J, Poludniowski G, Hansen VN (2015) Comparison of CT number calibration techniques for CBCT-based dose calculation. Strahlenther Onkol 191:970–978CrossRefGoogle Scholar
  13. 13.
    Brouwer CL, Steenbakkers RJHM, Langendijk JA, Sijtsema NM (2015) CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines. Radiother Oncol 117:83–90CrossRefGoogle Scholar
  14. 14.
    Toledano I, Graff P, Serre A, Boisselier P, Bensadoun RJ, Ortholan C (2012) Intensity-modulated radiotherapy in head and neck cancer: results of the prospective study GORTEC 2004–03. Radiother Oncol 103:57–62CrossRefGoogle Scholar
  15. 15.
    Low DA, Harms WB, Mutic S, Purdy JA (1998) A technique for the quantitative evaluation of dose distributions. Med Phys 25:656–661CrossRefGoogle Scholar
  16. 16.
    Hussein M, Clark CH, Nisbet A (2017) Challenges in calculation of the gamma index in radiotherapy – towards good practice. Phys Med 36:1–11CrossRefGoogle Scholar
  17. 17.
    Guan H, Dong H (2009) Dose calculation accuracy using cone-beam CT (CBCT) for pelvic adaptive radiotherapy. Phys Med Biol 54:6239–6250CrossRefGoogle Scholar
  18. 18.
    Hatton J, McCurdy B, Greer PB (2009) Cone beam computerized tomography: the effect of calibration of the Hounsfield unit number to electron density on dose calculation accuracy for adaptive radiation therapy. Phys Med Biol 54:N329–N346CrossRefGoogle Scholar
  19. 19.
    Thomas SJ (1999) Relative electron density calibration of CT scanners for radiotherapy treatment planning. Br J Radiol 72:781–786CrossRefGoogle Scholar
  20. 20.
    Cozzi L, Fogliata A, Buffa F, Bieri S (1998) Dosimetric impact of computed tomography calibration on a commercial treatment planning system for external radiation therapy. Radiother Oncol 48:335–338CrossRefGoogle Scholar
  21. 21.
    Schulze R (2011) Artefacts in CBCT: a review. Dentomaxillofac Radiol 40:265–273CrossRefGoogle Scholar
  22. 22.
    Richter A, Hu Q, Steglich D, Baier K, Wilbert J, Guckenberger M, Flentje M (2008) Investigation of the usability of conebeam CT data sets for dose calculation. Radiat Oncol.  https://doi.org/10.1186/1748-717x-3-42 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    van Zijtveld M, Dirkx M, Heijmen B (2007) Correction of conebeam CT values using a planning CT for derivation of the ’dose of the day. Radiother Oncol 85:195–200CrossRefGoogle Scholar
  24. 24.
    Ho KF, Marchant T, Moore C, Webster G, Rowbottom C, Penington H (2012) Monitoring dosimetric impact of weight loss with kilovoltage (KV) cone beam CT (CBCT) during parotid-sparing IMRT and concurrent chemotherapy. Int J Radiat Oncol Biol Phys 82:e375–e382CrossRefGoogle Scholar
  25. 25.
    Onozato Y, Kadoya N, Fujita Y, Arai K, Dobashi S, Takeda K (2014) Evaluation of on-board kV cone beam computed tomography–based dose calculation with deformable image registration using Hounsfield unit modifications. Int J Radiat Oncol Biol Phys 89:416–423CrossRefGoogle Scholar
  26. 26.
    Disher B, Hajdok G, Wang A, Craig J, Gaede S, Battista JJ (2013) Correction for ‘artificial’ electron disequilibrium due to cone-beam CT density errors: implications for on-line adaptive stereotactic body radiation therapy of lung. Phys Med Biol 58:4157–4174CrossRefGoogle Scholar
  27. 27.
    Karotki A, Mah K, Meijer G, Meltsner M (2011) Comparison of bulk electron density and voxel-based electron density treatment planning. J Appl Clin Med Phys 12:97–104CrossRefGoogle Scholar
  28. 28.
    Marchant TE, Joshi KD, Moore CJ (2018) Accuracy of radiotherapy dose calculations based on cone-beam CT: comparison of deformable registration and image correction based methods. Phys Med Biol.  https://doi.org/10.1088/1361-6560/aab0f0 CrossRefPubMedGoogle Scholar
  29. 29.
    Brivio D, Hu YD, Margalit DN, Zygmanski P (2018) Selection of head and neck cancer patients for adaptive replanning of radiation treatment using kV-CBCT. Biomed Phys Eng Express.  https://doi.org/10.1088/2057-1976/aad546 CrossRefGoogle Scholar
  30. 30.
    Rigaud B, Simon A, Castelli J, Gobeli M, Ospina Arango JD, Cazoulat G (2015) Evaluation of deformable image registration methods for dose monitoring in head and neck radiotherapy. Biomed Res Int.  https://doi.org/10.1155/2015/726268 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Pukala J, Johnson PB, Shah AP, Langen KM, Bova FJ, Staton RB (2016) Benchmarking of five commercial deformable image registration algorithms for head and neck patients. J Appl Clin Med Phys 17:25–40CrossRefGoogle Scholar
  32. 32.
    Castadot P, Lee JA, Parraga A, Geets X, Macq B, Grégoire V (2008) Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors. Radiother Oncol 89:1–12CrossRefGoogle Scholar
  33. 33.
    Li X, Zhang Y, Shi Y, Wu S, Xiao Y, Gu X (2017) 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 12:e175906CrossRefGoogle Scholar
  34. 34.
    La Macchia M, Fellin F, Amichetti M, Cianchetti M, Gianolini S, Paola V (2012) Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer. Radiat Oncol 7:160CrossRefGoogle Scholar
  35. 35.
    Hou J, Guerrero M, Chen W, D’Souza WD (2011) Deformable planning CT to cone-beam CT image registration in head-and-neck cancer. Med Phys.  https://doi.org/10.1118/1.3554647 CrossRefPubMedGoogle Scholar
  36. 36.
    Elstrøm UV, Wysocka BA, Muren LP, Petersen JBB, Grau C (2010) 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 Oncol 49:1101–1108CrossRefGoogle Scholar
  37. 37.
    Hvid CA, Elstrøm UV, Jensen K, Alber M, Grau C (2016) Accuracy of software-assisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy. Acta Oncol 55:1324–1330CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • A. Barateau
    • 1
  • N. Perichon
    • 1
  • J. Castelli
    • 1
  • U. Schick
    • 2
  • O. Henry
    • 1
  • E. Chajon
    • 1
  • A. Simon
    • 1
  • C. Lafond
    • 1
  • R. De Crevoisier
    • 1
  1. 1.Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099RennesFrance
  2. 2.Radiotherapy DepartmentCHU BrestBrestFrance

Personalised recommendations