Planning and Dosimetric Comparisons of IMRT Lung Cancers with Three Advanced Optimization Algorithms

Yie Chen, Jie Qu, Jack Yang, Mitch Weiss, Sang Sim, Xiongfei Liao

Department of Radiation Oncology, Beijing Union Hospital, Beijing, China.
Department of Radiation Oncology, Monmouth Medical Center, Long Branch, USA.
Department of radiation Oncology, Sichuan Cancer Hospital, Chengdu, China.

DOI: 10.4236/ijmpcero.2013.22008                

Abstract

Purpose: To evaluate planning quality and dosimetric differences of clinically deliverable Intensity-modulated Radiation Therapy lung plans generated from Tomotherapy, Pinnacle³, and RayStation^TM treatment planning systems. Method and Materials: Ten patients diagnosed with non-small-cell lung carcinoma (NSCLC) previously treated with plans on Pinnacle using Direct Machine Parameter Optimization were randomly selected and re-planned with Tomotherapy dose volume constraints and same beam geometry with RayStation Multi Criteria Optimization (MCO) equivalent uniform dose (EUD) or dose volume constraints, respectively.  Prescription was established as 60 Gy to cover > 95% of PTV. Planning outcomes such as D₉₅ (95% of volume of PTV receiving the prescribed dose), D_5, D_33, mean heart and lung doses, V₂₀ (volume of lung receiving 20 Gy), and max cord dose of 1cm³ were evaluated according to our departmental clinical protocols. Conformity index (CI = PTV / prescription isodose volume) and homogeneity index (HI = D₅/D₉₅) were also reported simultaneously. All plans were successfully uploaded for delivery verification. Results: Mean volume of calculated PTV was 356 ± 141 cm³. The planning results indicated that CI, HI, D₉₅ and D₅ of PTV, V₂₀ of lung, and 1cm³ max cord dose were comparable but with better overall dosimetric distributions with conformity and homogeneity index from Tomotherapy plans in comparison to both Pinnacle and RayStation planning outcomes. Conclusions: Tomotherapy plans achieved better uniform tumor coverage with fewer hot spots while sparing more critical structures with superior dose fall-off. RayStation plans with MCO automatically generated a set of Pareto optimized solutions with given objectives to allow tradeoffs between targets and critical organs and tended to achieve better tumor coverage compared to Pinnacle. All three planning algorithms can generate clinical deliverable IMRT lung plans while Tomotherapy plans provide superior dosimetric indexes compared to Pinnacle and RayStation due to its unique beamlet optimization process with high modulation.

Keywords

MCO; DMPO; Tomotherapy; Lung IMRT

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	T. Bortfeld, D. Kahler, T. Waldron and A. Boyer, “X-Ray Field Compensation with Multileaf Collimators,” International Journal of Radiation Oncology, Biology, Physics, Vol. 28, No. 3, 1994, pp. 723-730.
[2]	C. Chui, T. LoSasso and S. Spirou, “Dose Calculation for Photon Beam with Intensity Modulation Generated by Dynamic Jaw or Multileaf Collimation,” Medical Physics, Vol. 21, No. 8, 1994, pp. 1237-1244. doi:10.1118/1.597206
[3]	J. Galvin, X. Chen and R. Smith, “Combining Multileaf Field to Modualte Fluence Distributions,” International Journal of Radiation Oncology, Biology, Physics, Vol. 27, No. 3, 1993, pp. 697-705, doi:10.1016/0360-3016(93)90399-G
[4]	P. Xia and L. Verhey, “Multileaf Collimation Leaf Sequencing Algorithm for Intensity Modulated Beams with Multiple Static Segments,” Medical Physics, Vol. 25, No. 8, 1998, pp. 1424-1434. doi:10.1118/1.598315
[5]	W. Bar, M. Alber and F. Nusslin, “Fluence-Modulated Radiotherapy with an Optimization Integrated Sequencer,” Medical Physics, Vol. 13, No. 1, 2003, pp. 12-15.
[6]	D. Shepard, M. Earl, X. Li and C. Yu, “Direct Aperature Optimization: A Turnkey Solution for Step-and-Shoot IMRT,” Medical Physics, Vol. 29, No. 6, 2002, pp. 1007-1018. doi:10.1118/1.1477415
[7]	B. Hardemark, A, Liander, H. Rehbinder and J. Löf, “Direct Machine Parameter Optimization with RayMachine in Pinnacle,” RaySearch White Paper, 2003.
[8]	J. Löf, “Development of a General Framework for Optimization of Radiation Therapy,” PhD Thesis, Stockholm University, 2000. http://www.raysearchlabs.com/upload/Specialists/Articles_reports/JohanLofsThesis_LoRes.pdf
[9]	J. Rehbinder and H. Löf, “Inverse Planning Optimization with RayMachine in Pinnacle,” RaySearch White Paper, 2002. http://www.raysearchlabs.com/upload/Specialists/Solutions/White%20paper%20RayMachine.pdf
[10]	S. Tung, M. Lii, P. Lai and P. Wong, “Clinical Evaluation of Direct Machine Parameter Optimization Algorithm for Head and Neck IMRT Treatment,” Medical Physics, Vol. 32, No. 6, 2005, p. 1971. doi:10.1118/1.1997765
[11]	T. Hong, D. Craft, F. Carlsson and T. Bortfeld, “Multicriteria Optimization in Intensity-Modulated Radaition Therapy Treatment Planning for Locally Advanced Cancer of the Pancreatic Head,” International Journal of Radiation Oncology, Biology, Physics, Vol. 72, No. 4, 2008, pp. 1208-1214. doi:10.1016/j.ijrobp.2008.07.015
[12]	D. Craft, W. Chen, E. Salari, T. Madden and T. Bortfeld, “Multicriteria Optimization,” 2012. http://gray.mgh.harvard.edu/index.php?option=com_content&view=article&id=39:multicriteria-optimization&catid=7:optimization&Itemid=15
[13]	A. Niemierko, “Reporting and Analyzing Dose Distributions: A Concept of Equivalent Uniform Dose,” Medical Physics, Vol. 24, No. 1, 1997, pp. 103-110.
[14]	Q. Wu, R. Mohan, A. Niemierko and R. Schmidt-Ullrich, “Optimization of Intensity-Modulated Radiotherapy Plans Based on the Equivalent Uniform Dose,” International Journal of Radiation Oncology, Biology, Physics, Vol. 52, No. 1, 2002, pp. 224-235.
[15]	T. Holmes and T. Mackie, “Tomotherapy,” In: J. G. Webster, Ed., Encyclopedia of Medical Devices and Instrumentation, 2nd Edition, John Wiley & Sons, New York, 2006.
[16]	T. R. Mackie, “Tomotherapy: A New Concept for the Delivery of Dynamic Conformatl Radiothearpy,” Medical Physics, Vol. 20, No. 6, 1993, pp. 1709-1719. doi:10.1118/1.596958
[17]	T. Mackie, J. Balog, K. Ruchala, D. Shepard, S. Aldridge and E. Fitchard, “Radiation Therapy Treatment Optimization,” Seminars in Radiation Oncology, Vol. 9, No. 1, 1999, pp. 108-117,. doi:10.1016/S1053-4296(99)80058-7
[18]	T. Mackie, J. Balog, K. Ruchala, D. Shepard, S. Aldridge, E. Fitchard, P. Reckwerdt, G. Olivera, T. McNutt and M. Mehta, “Tomotherapy,” Seminars in Radiation Oncology, Vol. 9, No. 1, 1999, pp. 108-117. doi:10.1016/S1053-4296(99)80058-7
[19]	J. Welsh, R. Patel, M. Ritter, P. Harari, T. Mackie and M. Mehta, “Helical Tomotherapy: An Innovative Technology and Approach to Radiation Therapy,” Technology in Cancer Research and Treatment, Vol. 1, No. 4, 2002, pp. 311-316.
[20]	D. Shepard, G. Olivera and P. Reckwerdt, “Iterative Approaches to Dose Optimization in Tomotherapy,” Physics in Medicine and Biology, Vol. 45, No. 1, 2000, pp. 69-90. doi:10.1088/0031-9155/45/1/306
[21]	T. Knoos, I. Kristensen and P. Nilsson, “Volumetric and Dosimetric Evaluation of Radiation Treatment Plans: Radiation Conformity Index,” International Journal of Radiation Oncology, Biology, Physics, Vol. 42, No. 5, 1998, pp. 1169-1176. doi:10.1016/S0360-3016(98)00239-9
[22]	R. Scrimger, W. Tome, G. Olivera, P. Reckwerdt, M. Mehta and J. Fowler, “Reduction in Radiation Dose to Lung and Other Normal Tissues Using Helical Tomotherapy to Treat Lung Cancer, in Comparison to Conventional Field Arrangements,” American Journal of Clinical Oncology, Vol. 26, No. 1, 2003, pp. 70-78. doi:10.1097/00000421-200302000-00014