IMRT QA using machine learning: A multi-institutional validation

Journal of Applied Clinical Medical Physics

Volumn 18, Issue 5, 2017, Pages 279-284

  Valdes, Gilmer ^a,c   Chan, Maria F ^b   Lim, Seng Boh ^b   Scheuermann, Ryan ^c   Deasy, Joseph O ^b   Solberg, Timothy D ^a,c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

IMRT QA; Machine learning; Poisson regression; Radiotherapy

Indexed keywords

CLINICAL TRIAL; HEALTH CARE QUALITY; HUMAN; INTENSITY MODULATED RADIATION THERAPY; MACHINE LEARNING; MULTICENTER STUDY; RADIOMETRY; RADIOTHERAPY DOSAGE; STANDARDS; VALIDATION STUDY;

HUMANS; MACHINE LEARNING; QUALITY ASSURANCE, HEALTH CARE; RADIOMETRY; RADIOTHERAPY DOSAGE; RADIOTHERAPY, INTENSITY-MODULATED;

EID: 85027501141     PISSN: None     EISSN: 15269914     Source Type: Journal    
DOI: 10.1002/acm2.12161     Document Type: Article

References (25)

1. Mihailidis D, Phillips MH. Machine Learning in Radiation Oncology: Theory and Applications. New York, NY: Springer; 2017.
2. Zhang HH, D'Souza WD, Shi L, Meyer RR. Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework. Int J Radiat Oncol Biol Phys. 2009;74:1617-1626.
3. Valentini V, Dinapoli N, Damiani A. The future of predictive models in radiation oncology: from extensive data mining to reliable modeling of the results. Future Oncol. 2013;9:311-313.
4. Robertson SP, Quon H, Kiess AP, et al. A data-mining framework for large scale analysis of dose-outcome relationships in a database of irradiated head and neck cancer patients. Med Phys. 2015;42:4329-4337.
5. Kang J, Schwartz R, Flickinger J, Beriwal S. Machine learning approaches for predicting radiation therapy outcomes: a clinician's perspective. Int J Radiat Oncol Biol Phys. 2015;93:1127-1135.
6. Oermann EK, Rubinsteyn A, Ding D, et al. Using a machine learning approach to predict outcomes after radiosurgery for cerebral arteriovenous malformations. Sci Rep. 2016;6:21161.
7. Valdes G, Solberg TD, Heskel M, Ungar L, Simone CB II. Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy. Phys Med Biol. 2016;61:6105.
8. Zhang J, Ates O, Li A. Implementation of a machine learning-based automatic contour quality assurance tool for online adaptive radiation therapy of prostate cancer. Int J Radiat Oncol Biol Phys. 2016;96:E668.
9. Schreibmann E, Fox T. Prior-knowledge treatment planning for volumetric arc therapy using feature-based database mining. J Appl Clinic Med Phys. 2014;15:19-27.
10. Price S, Golden B, Wasil E, Zhang HH. Data mining to aid beam angle selection for intensity-modulated radiation therapy. Paper presented at: Proceedings of the 5th ACM conference on bioinformatics, computational biology, and health informatics. 2014.
11. Stanhope C, Wu QJ, Yuan L, et al. Utilizing knowledge from prior plans in the evaluation of quality assurance. Phys Med Biol. 2015;60:4873.
12. Carlson JN, Park JM, Park S-Y, Park JI, Choi Y, Ye S-J. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors. Phys Med Biol. 2016;61:2514.
13. Tomatis S, Palumbo V, D'Agostino G, et al. EP-1329: data mining applied to a radiotherapy department: developing quality assurance tools for risk management. Radiother Oncol. 2015;115:S718.
14. Bojechko C, Ford E. Quantifying the performance of in vivo portal dosimetry in detecting four types of treatment parameter variations. Med Phys. 2015;42:6912-6918.
15. Ford EC, Terezakis S, Souranis A, Harris K, Gay H, Mutic S. Quality control quantification (QCQ): a tool to measure the value of quality control checks in radiation oncology. Int J Radiat Oncol Biol Phys. 2012;84:e263-e269.
16. Li Q, Chan MF. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study. Ann N Y Acad Sci. 2017;1387:84-94.
17. Chan MF, Li Q, Tang X, et al. Visual analysis of the daily QA results of photon and electron beams of a trilogy linac over a five-year period. Int J Med Phys Clin Eng Radiat Oncol. 2015;4:290.
18. El Naqa I. SU-E-J-69: an anomaly detector for radiotherapy quality assurance using machine learning. Med Phys. 2011;38:3458-3458.
19. Valdes G, Morin O, Valenciaga Y, Kirby N, Pouliot J, Chuang C. Use of TrueBeam developer mode for imaging QA. J Appl Clin Med Phys. 2015;16:322-333.
20. Valdes G, Scheuermann R, Hung C, Olszanski A, Bellerive M, Solberg T. A mathematical framework for virtual IMRT QA using machine learning. Med Phys. 2016;43:4323-4334.
21. Huq MS, Fraass BA, Dunscombe PB, et al. The report of Task Group 100 of the AAPM: application of risk analysis methods to radiation therapy quality management. Med Phys. 2016;43:4209-4262.
22. Hastie T, Friedman JH, Tibshirani R. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Stanford, CA: Springer; 2009.
23. Vial P, Greer PB, Hunt P, Oliver L, Baldock C. The impact of MLC transmitted radiation on EPID dosimetry for dynamic MLC beams. Med Phys. 2008;35:1267-1277.
24. Hobson MA, Davis SD. Comparison between an in-house 1D profile correction method and a 2D correction provided in Varian's PDPC Package for improving the accuracy of portal dosimetry images. J Appl Clinic Med Phys. 2015;16:43-50.
25. Nelms BE, Chan MF, Jarry G, et al. Evaluating IMRT and VMAT dose accuracy: practical examples of failure to detect systematic errors when applying a commonly used metric and action levels. Med Phys. 2013;40:111722-111737.