Quantitative Metrics for Assessing Plan Quality

Seminars in Radiation Oncology

Volumn 22, Issue 1, 2012, Pages 62-69

  Moore, Kevin L ^a   Brame, R Scott ^a   Low, Daniel A ^b   Mutic, Sasa ^a  

^a WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN; INTENSITY MODULATED RADIATION THERAPY; PRIORITY JOURNAL; QUALITY CONTROL; REVIEW; TREATMENT PLANNING;

EID: 83455259597     PISSN: 10534296     EISSN: 15329461     Source Type: Journal    
DOI: 10.1016/j.semradonc.2011.09.005     Document Type: Review

References (55)

1. Schultheiss T.E., Orton C.G. Models in radiotherapy: Definition of decision criteria. Med Phys 1985, 12:183-187.
2. Jain N.L., Kahn M.G., Drzymala R.E., et al. Objective evaluation of 3-D radiation treatment plans: A decision-analytic tool incorporating treatment preferences of radiation oncologists. Int J Radiat Oncol Biol Phys 1993, 26:321-333.
3. Willoughby T.R., Starkschall G., Janjan N.A., et al. Evaluation and scoring of radiotherapy treatment plans using an artificial neural network. Int J Radiat Oncol Biol Phys 1996, 34:923-930.
4. Langer M., Morrill S.S., Lane R. A test of the claim that plan rankings are determined by relative complication and tumor-control probabilities. Int J Radiat Oncol Biol Phys 1998, 41:451-457.
5. Amols H.I., Zaider M., Hayes M.K., et al. Physician/patient-driven risk assignment in radiation oncology: Reality or fancy?. Int J Radiat Oncol Biol Phys 1997, 38:455-461.
6. Miften M.M., Das S.K., Su M., et al. A dose-volume-based tool for evaluating and ranking IMRT treatment plans. J Appl Clin Med Phys 2004, 5:1-14.
7. Meyer R.R., Zhang H.H., Goadrich L., et al. A multiplan treatment-planning framework: A paradigm shift for intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys 2007, 68:1178-1189.
8. Bentzen S.M., Constine L.S., Deasy J.O., et al. Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC). An Introduction to the Scientific Issues. Int J Radiat Oncol Bio Phys 2010, 76:S3-S9.
9. Balter J.M., Kessler M.L. Imaging and alignment for image-guided radiation therapy. J Clin Oncol 2007, 25:931-937.
10. Shan Z.Y., Mateja S.J., Reddick W.E., et al. Retrospective evaluation of PET-MRI registration algorithms. J Digit Imaging 2011, 24:485-493.
11. Kashani R., Hub M., Kessler M.L., et al. Technical note: a physical phantom for assessment of accuracy of deformable alignment algorithms. Med Phys 2007, 34:2785-2788.
12. Kashani R., Lam K., Litzenberg D., et al. Technical note: a deformable phantom for dynamic modeling in radiation therapy. Med Phys 2007, 34:199-201.
13. Brock K.K. Results of a multi-institution deformable registration accuracy study (MIDRAS). Int J Radiat Oncol Biol Phys 2010, 76:583-596. Deformable Registration Accuracy Consortium.
14. van Herk M., Remeijer P., Lebesque J.V. Inclusion of geometric uncertainties in treatment plan evaluation. Int J Radiat Oncol Biol Phys 2002, 52:1407-1422.
15. Weiss E., Hess C.F. The impact of gross tumor volume (GTV) and clinical target volume (CTV) definition on the total accuracy in radiotherapy-Theoretical aspects and practical experiences. Strahlenther Onkol 2003, 179:21-30.
16. Njeh C.F. Tumor delineation: The weakest link in the search for accuracy in radiotherapy. J Med Phys 2008, 33:136-140.
17. Moore K.L. DTA-based metrics for the evaluation of autosegmentation algorithms in clinical radiotherapy workflow. Med Phys 2009, 36:2454.
18. Villeirs G.M., Van Vaerenbergh K., Vakaet L., et al. Interobserver delineation variation using CT versus combined CT plus MRI in intensity-modulated radiotherapy for prostate cancer. Strahlenther Onkol 2005, 181:424-430.
19. Hong T.S., Chappell R.J., Harari P.M. Variations in target delineation for head and neck IMRT: an international multi-institutional study. Int J Radiat Oncol Biol Phys 2004, 60:S157.
20. Chao K.S., Bhide S., Chen H., et al. Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. Int J Radiat Oncol Biol Phys 2007, 68:1512-1521.
21. Dice L.R., Blossom P.M. Studies of Mammalian Ecology in Southwestern North America, With Special Attention to the Colors of Desert Mammals. Carnegie Institution of Washington. Publication no. 485 1937, 129. Carnegie Institution of Washington, Washington, DC.
22. Drzymala R.E., Mohan R., Brewster L., et al. Dose-volume histograms. Int J Radiat Oncol Biol Phys 1991, 21:71-78.
23. Niemierko A. Reporting and analyzing dose distributions: a concept of equivalent uniform dose. Med Phys 1997, 24:103-110.
24. Artacho Terrer J.M., Nasarre Benedé M.A., Bernués del Rio E., et al. A feasible application of constrained optimization in the IMRT system. IEEE Trans Biomed Eng 2007, 54:370-379.
25. Monz M., Küfer K.H., Bortfeld T.R., et al. Pareto navigation: algorithmic foundation of interactive multi-criteria IMRT planning. Phys Med Biol 2008, 53:985-998.
26. Holdsworth C, Stewart RD, Kim M, et al: Investigation of effective decision criteria for multiobjective optimization in IMRT. Med Phys 38:2964-2974.
27. Holdsworth C, Kim M, Liao J, et al: A hierarchical evolutionary algorithm for multiobjective optimization in IMRT. Med Phys 37:4986-4997.
28. Halabi T., Craft D., Bortfeld T. Dose-volume objectives in multi-criteria optimization. Phys Med Biol 2006, 51:3809-3818.
29. Lu R., Radke R.J., Yang J., et al. Reduced-order constrained optimization in IMRT planning. Phys Med Biol 2008, 53:6749-6766.
30. Moore K.L., et al. Experience-based quality control of clinical IMRT planning. Int J Radiat Oncol Biol Phys 2011, (in press).
31. Wu B., Ricchetti F., Sanguineti G., et al. Patient geometry-driven information retrieval for IMRT treatment plan quality control. Med Phys 2009, 36:5497-5505.
32. Vrancić C., Trofimov A., Chan T.C., et al. Experimental evaluation of a robust optimization method for IMRT of moving targets. Phys Med Biol 2009, 54:2901-2914.
33. Chan T.C., Bortfeld T., Tsitsiklis J.N. A robust approach to IMRT optimization. Phys Med Biol 2006, 51:2567-2583.
34. Sripadam R., Stratford J., Henry A.M., et al. Rectal motion can reduce CTV coverage and increase rectal dose during prostate radiotherapy: A daily cone-beam CT study. Radiother Oncol 2009, 90:312-317.
35. Moore K.L., Brame R.S., Low D.A., et al. Experience-based quality control of clinical intensity-modulated radiotherapy planning. Int J Radiat Oncol Biol Phys 2011, 81:545-551.
36. Pawlicki T., Mundt A.J. Quality in radiation oncology. Med Phys 2007, 34:1529-1534.
37. De Feo J.A. JURAN Institute's Six Sigma Breakthrough and Beyond-Quality Performance Breakthrough Methods 2005, McGraw-Hill, New York.
38. Deasy J.O., Blanco A.I., Clark V.H. CERR: a computational environment for radiotherapy research. Med Phys 2003, 30:979-985.
39. Bradley J.D., Hope A., El Naqa I., et al. A nomogram to predict radiation pneumonitis, derived from a combined analysis of RTOG 9311 and institutional data. Int J Radiat Oncol Biol Phys 2007, 69:985-992.
40. McNutt T., Wong J., Purdy, et al. OncoSpace: A new paradigm for clinical research and decision support in radiation oncology. International Conference on the Use of Computers in Radiation Therapy 2010, Amsterdam, The Netherlands.
41. Hopkins S., Oakes L., Upasani, et al. Radiation Oncology Data Alliance: A Scalable Framework for a Modality-Wide Database Suitable for Comparative Effectiveness Research. International Conference on the Use of Computers in radiation therapy 2010, Amsterdam, The Netherlands.
42. Mullen D., Apte A., Khullar, et al. Development of a comprehensive Outcomes Quality Assurance (OQA) databank for radiation oncology. International Conference on the Use of Computers in radiation therapy 2010, Amsterdam, The Netherlands.
43. Persoon L.C.G.G., et al. Advantages of a data warehouse for radiotherapy research. International Conference on the Use of Computers in radiation therapy 2010, Amsterdam, The Netherlands.
44. Geoghegan S. Scripting in the Pinnacle3 Treatment Planning System, 2007 Accessed in March 2011. http://www.medphysfiles.com.
45. Yang D., Moore K.L. Radiotherapy plan integrity verification with dynamic pinnacle scripts. Med Phys 2010, 37:3381.
46. Bosch W.R., Curran B.H., Swedloff S. IHE-RO: Interoperable Data Standards for Radiation Oncology, International Conference on the Use of Computers in Radiation Therapy 2010, Amsterdam, The Netherlands.
47. Das S. A role for biological optimization within the current treatment planning paradigm. Med Phys 2009, 36:4672-4682.
48. Ibbott G.S., et al. Challenges in credentialing institutions and participants in advanced technology multi-institutional clinical trials. Int J Radiat Oncol Biol Phys 2008, 71:S71-S75.
49. Vineberg K.A., Eisbruch A., Coselmon M.M., et al. Is uniform target dose possible in IMRT plans in the head and neck?. Int J Radiat Oncol Biol Phys 2002, 52:1159-1172.
50. Constine L.S., et al. Late effects of cancer treatment on normal tissues 2008, 320-355. Principles of Radiation Oncology, Philadelphia, PA. E.C. Halperin, C.A. Perez, L.W. Brady (Eds.).
51. Clark V.H., Chen Y., Wilkens J., et al. IMRT treatment planning for prostate cancer using prioritized prescription optimization and mean-tail-dose functions. Linear Algebra Appl 2008, 428:1345-1364.
52. Jee K.W., McShan D.L., Fraass B.A. Lexicographic ordering: intuitive multicriteria optimization for IMRT. Phys Med Biol 2007, 52:1845-1861.
53. Serna J.I., Monz M., Küfer K.H., et al. Trade-off bounds for the Pareto surface approximation in multi-criteria IMRT planning. Phys Med Biol 2009, 54:6299-6311.
54. Hong T.S., Craft D.L., Carlsson F., et al. Multicriteria optimization in intensity-modulated radiation therapy treatment planning for locally advanced cancer of the pancreatic head. Int J Radiat Oncol Biol Phys 2008, 72:1208-1214.
55. Hoffmann A.L., Siem A.Y., den Hertog D., et al. Derivative-free generation and interpolation of convex Pareto optimal IMRT plans. Phys Med Biol 2006, 51:6349-6369.