Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making

Radiotherapy and Oncology

Volumn 125, Issue 3, 2017, Pages 392-397

  Valdes, Gilmer ^a   Simone, Charles B ^b   Chen, Josephine ^a   Lin, Alexander ^c   Yom, Sue S ^a,d   Pattison, Adam J ^e   Carpenter, Colin M ^e   Solberg, Timothy D ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF MARYLAND MEDICAL CENTER   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

^d Department of Otolaryngology Head and Neck Surgery ^*  (United States)

^e Siris Medical Inc   (United States)

Author keywords

Decision support system; Dosimetric tradeoffs; Knowledge based planning

Indexed keywords

ACCURACY; ARTICLE; CANCER RADIOTHERAPY; CLASSIFIER; CLINICAL DECISION SUPPORT SYSTEM; DOSIMETRY; EARLY CANCER; HUMAN; INTENSITY MODULATED RADIATION THERAPY; LEARNING CURVE; LUNG CANCER; MAJOR CLINICAL STUDY; OROPHARYNX CANCER; PATIENT DECISION MAKING; PHOTON THERAPY; PRIORITY JOURNAL; PROTON THERAPY; RETROSPECTIVE STUDY; STEREOTACTIC BODY RADIATION THERAPY; TREATMENT PLANNING; DECISION MAKING; MACHINE LEARNING; OROPHARYNX TUMOR; PROCEDURES; RADIOTHERAPY PLANNING SYSTEM;

DECISION MAKING; DECISION SUPPORT SYSTEMS, CLINICAL; HUMANS; MACHINE LEARNING; OROPHARYNGEAL NEOPLASMS; RADIOTHERAPY PLANNING, COMPUTER-ASSISTED;

EID: 85034827908     PISSN: 01678140     EISSN: 18790887     Source Type: Journal    
DOI: 10.1016/j.radonc.2017.10.014     Document Type: Article

References (27)

1. Simone, C.B. 2nd, et al. Comparison of intensity-modulated radiotherapy, adaptive radiotherapy, proton radiotherapy, and adaptive proton radiotherapy for treatment of locally advanced head and neck cancer. Radiother Oncol 101 (2011), 376–382.
2. Garg, A.X., et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. Jama 293 (2005), 1223–1238.
3. Burnside, E.S., et al. Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings 1. Radiology 251 (2009), 663–672.
4. Valdes, G., et al. MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine. Sci Rep, 6, 2016, 37854.
5. Caruana, R., et al. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 201ACM.
6. Li, Q., Chan, M.F., Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study. Ann N Y Acad Sci 1387 (2017), 84–94.
7. Valdes, G., et al. A mathematical framework for virtual IMRT QA using machine learning. Med Phys 43 (2016), 4323–4334.
8. Valdes, G., et al. Use of TrueBeam developer mode for imaging QA. J Appl Clin Med Phys, 16, 2015, 5363.
9. Valdes, G., et al. 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, 61, 2016, 6105.
10. El Naqa, I., et al. Datamining approaches for modeling tumor control probability. Acta Oncol 49 (2010), 1363–1373.
11. El Naqa, I., et al. Predicting radiotherapy outcomes using statistical learning techniquesPart of this work was first presented at the Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, 11–13 December 2008. Phys Med Biol, 54, 2009, S9.
12. Lambin, P., et al. Rapid Learning health care in oncology’–an approach towards decision support systems enabling customised radiotherapy. Radiother Oncol 109 (2013), 159–164.
13. Oberije, C., et al. A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making. Radiother Oncol 112 (2014), 37–43.
14. Zhu, X., et al. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Med Phys 38 (2011), 719–726.
15. Moore, K.L., et al. Experience-based quality control of clinical intensity-modulated radiotherapy planning. Int J Radiat Oncol Biol Phys 81 (2011), 545–551.
16. Good, D., et al. A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning. Int J Radiat Oncol Biol Phys 87 (2013), 176–181.
17. Tol, J.P., et al. Evaluation of a knowledge-based planning solution for head and neck cancer. Int J Radiat Oncol Biol Phys 91 (2015), 612–620.
18. Wu, B., et al. Using overlap volume histogram and IMRT plan data to guide and automate VMAT planning: a head‐and‐neck case study. Med Phys, 40, 2013.
19. Friedman, J., Hastie, T., Tibshirani, R., The elements of statistical learning. Springer series in statistics, 2001, Springer, Berlin.
20. Friedman, J.H., Greedy function approximation: a gradient boosting machine. Ann Stat, 2001, 1189–1232.
21. Forsberg, J.A., et al. Estimating survival in patients with operable skeletal metastases: an application of a bayesian belief network. PLoS One, 6, 2011, e19956.
22. Blanchard, P., et al. Toward a model-based patient selection strategy for proton therapy: external validation of photon-derived normal tissue complication probability models in a head and neck proton therapy cohort. Radiother Oncol 121 (2016), 381–386.
23. Langendijk, J.A., et al. Selection of patients for radiotherapy with protons aiming at reduction of side effects: the model-based approach. Radiother Oncol 107 (2013), 267–273.
24. Brodin, N.P., et al. Interactive decision-support tool for risk-based radiation therapy plan comparison for Hodgkin lymphoma. Int J Radiat Oncol Biol Phys 88 (2014), 433–445.
25. Cheng, Q., et al. Development and evaluation of an online three-level proton vs photon decision support prototype for head and neck cancer–comparison of dose, toxicity and cost-effectiveness. Radiother Oncol 118 (2016), 281–285.
26. Craft, D., Bortfeld, T., How many plans are needed in an IMRT multi-objective plan database?. Phys Med Biol, 53, 2008, 2785.
27. Thieke, C., et al. A new concept for interactive radiotherapy planning with multicriteria optimization: first clinical evaluation. Radiother Oncol 85 (2007), 292–298.