Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Radiotherapy and Oncology

Volumn 129, Issue 3, 2018, Pages 421-426

  Thompson, Reid F ^a,b   Valdes, Gilmer ^c   Fuller, Clifton D ^d   Carpenter, Colin M ^e   Morin, Olivier ^c   Aneja, Sanjay ^f   Lindsay, William D ^g   Aerts, Hugo J W L ^h,i   Agrimson, Barbara ^a   Deville, Curtiland ^j   Rosenthal, Seth A ^k   Yu, James B ^f   Thomas, Charles R ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^b PORTLAND VA MEDICAL CENTER   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^e Siris Medical Inc   (United States)

^f YALE UNIVERSITY   (United States)

^g Oncora Medical   (United States)

^h BRIGHAM AND WOMEN S HOSPITAL   (United States)

ⁱ DANA FARBER CANCER INSTITUTE   (United States)

^j JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^k Sutter Medical Group and Suttter Cancer Center   (United States)

more..

Author keywords

Artificial intelligence; Deep learning; Machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLINICAL DECISION SUPPORT SYSTEM; CLINICAL OUTCOME; DOSIMETRY; HUMAN; IMAGE SEGMENTATION; MEDICAL EDUCATION; PATIENT CARE; PHYSICS; PREDICTION; PRIORITY JOURNAL; QUALITY CONTROL; RADIATION ONCOLOGY; RADIOTHERAPY DOSAGE; REVIEW; DECISION SUPPORT SYSTEM; FORECASTING; HEALTH CARE QUALITY; MACHINE LEARNING; PROCEDURES; RADIOLOGY;

ARTIFICIAL INTELLIGENCE; DECISION SUPPORT TECHNIQUES; FORECASTING; HUMANS; MACHINE LEARNING; PATIENT CARE; QUALITY ASSURANCE, HEALTH CARE; RADIATION ONCOLOGY; RADIOLOGY; RADIOTHERAPY DOSAGE;

EID: 85048335962     PISSN: 01678140     EISSN: 18790887     Source Type: Journal    
DOI: 10.1016/j.radonc.2018.05.030     Document Type: Review

References (89)

1. Hoagland, J., The fourth industrial revolution is upon us. 2017, The Washington Post.
2. DeepMind. Applying machine learning to radiotherapy planning for head & neck cancer. (2016). Available at: https://deepmind.com/blog/applying-machine-learning-radiotherapy-planning-head-neck-cancer/. (Accessed: 1st January 2016).
3. Valdes, G., et al. Clinical decision support of radiotherapy treatment planning: a data-driven machine learning strategy for patient-specific dosimetric decision making. Radiother Oncol 125 (2017), 392–397, 10.1016/j.radonc.2017.10.014.
4. Aljabar, P., Gooding, M.J., The cutting edge: delineating contours with Deep. Learning, 2017.
5. Oncora Medical. Available at: https://oncoramedical.com/. (Accessed: 1st November 2017).
6. Bibault, J.-E., Giraud, P., Burgun, A., Big Data and machine learning in radiation oncology: state of the art and future prospects. Cancer Lett 382 (2016), 110–117.
7. Obermeyer, Z., Lee, T.H., Lost in thought—The limits of the human mind and the future of medicine. N Engl J Med 377 (2017), 1209–1211.
8. Chen, J.H., Asch, S.M., Machine learning and prediction in medicine – beyond the peak of inflated expectations. N Engl J Med 376 (2017), 2507–2509.
9. Jepsen, P., Johnsen, S.P., Gillman, M.W., Sørensen, H.T., Interpretation of observational studies. Heart 90 (2004), 956–960.
10. Cabitza, F., Rasoini, R., Gensini, G.F., Unintended consequences of machine learning in medicine. JAMA 318 (2017), 517–518.
11. Clark, M.C., et al. Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imaging 17 (1998), 187–201.
12. Project InnerEye – Medical Imaging AI to Empower Clinicians. Available at: https://www.microsoft.com/en-us/research/project/medical-image-analysis/. (Accessed: 1st November 2017).
13. Kamnitsas, K. et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. (2016).
14. Walker, G.V., et al. Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer. Radiother Oncol 112 (2014), 321–325.
15. Ibragimov, B., Xing, L., Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys 44 (2017), 547–557.
16. Delpon, G., et al. Comparison of automated atlas-based segmentation software for postoperative prostate cancer radiotherapy. Front Oncol, 6, 2016, 178.
17. 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.
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, 21714.
19. Moore, K.L., Brame, R.S., Low, D.A., Mutic, S., Experience-based quality control of clinical intensity-modulated radiotherapy planning. Int J Radiat Oncol Biol Phys 81 (2011), 545–551.
20. Nwankwo, O., Mekdash, H., Sihono, D.S.K., Wenz, F., Glatting, G., Knowledge-based radiation therapy (KBRT) treatment planning versus planning by experts: validation of a KBRT algorithm for prostate cancer treatment planning. Radiat Oncol, 10, 2015, 111.
21. Schubert, C., et al. Intercenter validation of a knowledge based model for automated planning of volumetric modulated arc therapy for prostate cancer. The experience of the German RapidPlan Consortium. PLoS One, 12, 2017, e0178034.
22. Li, N., et al. Highly efficient training, refinement, and validation of a knowledge-based planning quality-control system for radiation therapy clinical trials. Int J Radiat Oncol Biol Phys 97 (2017), 164–172.
23. Chatterjee, A., et al. Performance of knowledge-based radiation therapy planning for the glioblastoma disease site. Int J Radiat Oncol, 2017, 10.1016/j.ijrobp.2017.07.012.
24. Tol, J.P., Delaney, A.R., Dahele, M., Slotman, B.J., Verbakel, W.F.A.R., Evaluation of a knowledge-based planning solution for head and neck cancer. Int J Radiat Oncol Biol Phys 91 (2015), 612–620.
25. Foy, J.J., et al. An analysis of knowledge-based planning for stereotactic body radiation therapy of the spine. Pract Radiat Oncol, 2017, 10.1016/j.prro.2017.02.007.
26. Zhu, X., et al. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Med Phys 38 (2011), 719–726.
27. Appenzoller, L.M., Michalski, J.M., Thorstad, W.L., Mutic, S., Moore, K.L., Predicting dose-volume histograms for organs-at-risk in IMRT planning. Med Phys 39 (2012), 7446–7461.
28. Moore, K.L., et al. Quantifying unnecessary normal tissue complication risks due to suboptimal planning: a secondary study of RTOG 0126. Int J Radiat Oncol Biol Phys 92 (2015), 228–235.
29. Chang, A.T.Y., et al. Comparison of planning quality and efficiency between conventional and knowledge-based algorithms in nasopharyngeal cancer patients using intensity modulated radiation therapy. Int J Radiat Oncol Biol Phys 95 (2016), 981–990.
30. 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.
31. 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.
32. Hall, D.C., Trofimov, A.V., Winey, B.A., Liebsch, N.J., Paganetti, H., Predicting patient-specific dosimetric benefits of proton therapy for skull-base tumors using a geometric knowledge-based method. Int J Radiat Oncol Biol Phys 97 (2017), 1087–1094.
33. Naqa, I.E., et al. Datamining approaches for modeling tumor control probability. Acta Oncol 49 (2010), 1363–1373.
34. Naqa, I.E., et al. Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors. Int J Radiat Oncol Biol Phys 64 (2006), 1275–1286.
35. 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.
36. Bradley, J., Deasy, J.O., Bentzen, S., Naqa, I.E., Dosimetric correlates for acute esophagitis in patients treated with radiotherapy for lung carcinoma. Int J Radiat Oncol Biol Phys 58 (2004), 1106–1113.
37. Hope, A.J., et al. Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters. Int J Radiat Oncol Biol Phys 65 (2006), 112–124.
38. Huynh, E., et al. CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiother Oncol 120 (2016), 258–266.
39. Deist, T.M., et al. Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT. Clin Transl Radiat Oncol 4 (2017), 24–31.
40. OncoSpace. Available at: https://oncospace.radonc.jhmi.edu/. (Accessed: 1st December 2017).
41. Flatiron Health. Available at: https://flatiron.com/. (Accessed: 1st November 2017).
42. 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.
43. Reich, C., Ryan, P., Belenkaya, R., Natarajan, K. & Blacketer, C. OMOP Common Data Model v5.2 Specifications. (2017). Available at: https://github.com/OHDSI/CommonDataModel/blob/master/OMOP_CDM_v5_2.pdf.
44. Dekker, A. Radiation Oncology Ontology. Available at: http://bioportal.bioontology.org/ontologies/ROO.
45. Phillips, M., Halasz, L., Radiation oncology needs to adopt a comprehensive standard for data transfer: the case for HL7 FHIR. Int J Radiat Oncol Biol Phys 99 (2017), 1073–1075.
46. Rosenbloom, S.T., Carroll, R.J., Warner, J.L., Matheny, M.E., Denny, J.C., Representing knowledge consistently across health systems. Yearb Med Inform 26 (2017), 139–147.
47. Valdes, G., et al. A mathematical framework for virtual IMRT QA using machine learning. Med Phys, 43, 2016, 4323.
48. Valdes, G., et al. IMRT QA using machine learning: a multi-institutional validation. J Appl Clin Med Phys 18 (2017), 279–284.
49. Valdes, G., et al. Use of TrueBeam developer mode for imaging QA. J Appl Clin Med Phys 16 (2015), 322–333.
50. 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.
51. Hoisak, J.D.P., Pawlicki, T., Kim, G.-Y., Fletcher, R., Moore, K.L., Improving linear accelerator service response with a real- time electronic event reporting system. J Appl Clin Med Phys, 15, 2014, 4807.
52. Chen, J.H., Alagappan, M., Goldstein, M.K., Asch, S.M., Altman, R.B., Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int J Med Inform 102 (2017), 71–79.
53. Lim-Reinders, S., Keller, B.M., Al-Ward, S., Sahgal, A., Kim, A., Online adaptive radiation therapy. Int J Radiat Oncol Biol Phys 99 (2017), 994–1003.
54. Hoff, T., Deskilling and adaptation among primary care physicians using two work innovations. Health Care Manage Rev 36 (2011), 338–348.
55. Dial, C., Weiss, E., Siebers, J.V., Hugo, G.D., Benefits of adaptive radiation therapy in lung cancer as a function of replanning frequency. Med Phys, 43, 2016, 1787.
56. Chen, A.M., et al. Clinical outcomes among patients with head and neck cancer treated by intensity-modulated radiotherapy with and without adaptive replanning. Head Neck 36 (2014), 1541–1546.
57. Tang, X., Wang, B., Rong, Y., Artificial intelligence will reduce the need for clinical medical physicists. J. Appl Clin Med Phys 19 (2018), 6–9.
58. Atwood, T.F., et al. Care for patients, not for charts: a future for clinical medical physics. Int J Radiat Oncol Biol Phys 100 (2018), 21–22.
59. Sullivan, T., Half of hospitals to adopt artificial intelligence within 5 years. 2017, Healthcare IT News.
60. Hill, S.Y., Data sharing: guard the privacy of donors. Nature, 548, 2017, 281.
61. Kayaalp, M., Patient privacy in the era of big data. Balkan Med J, 2017, 10.4274/balkanmedj.2017.0966.
62. Skripcak, T., et al. Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasets. Radiother Oncol 113 (2014), 303–309.
63. Jochems, A., et al. Distributed learning: developing a predictive model based on data from multiple hospitals without data leaving the hospital – a real life proof of concept. Radiother Oncol 121 (2016), 459–467.
64. Gutmann, A., Data re-identification: prioritize privacy. Science, 339, 2013, 1032.
65. Rumbold, J.M.M., Pierscionek, B., The effect of the general data protection regulation on medical research. J Med Internet Res, 19, 2017, e47.
66. European Society of Radiology (ESR). The new EU General Data Protection Regulation: what the radiologist should know. Insights Imaging 2017;8:295–299.
67. McGoon, C., NHS illegally handed Google firm 1.6m patient records, UK data watchdog finds. 2017, The Telegraph.
68. Bak, K., Dobrow, M.J., Hodgson, D., Whitton, A., Factors affecting the implementation of complex and evolving technologies: multiple case study of intensity-modulated radiation therapy (IMRT) in Ontario Canada. BMC Health Serv Res, 11, 2011, 178.
69. Rassiah-Szegedi, P., et al. Monte Carlo characterization of target doses in stereotactic body radiation therapy (SBRT). Acta Oncol 45 (2006), 989–994.
70. Latifi, K., et al. Study of 201 non-small cell lung cancer patients given stereotactic ablative radiation therapy shows local control dependence on dose calculation algorithm. Int J Radiat Oncol Biol Phys 88 (2014), 1108–1113.
71. ACR Data Science Institute™ to Guide Artificial Intelligence Use in Medical Imaging. (2017). Available at: https://www.acr.org/About-Us/Media-Center/Press-Releases/2017-Press-Releases/20170521-ACR-Data-Science-Institute-to-Guide-Artificial-Intelligence-Use-in-Medical-Imaging.
72. Kim, D., Knowledge sharing as a social dilemma in pharmaceutical innovation. Food Drug Law J 71 (2016), 673–709.
73. Bertagnolli, M.M., et al. Advantages of a truly open-access data-sharing model. N Engl J Med 376 (2017), 1178–1181.
74. Figueiredo, A.S., Data sharing: convert challenges into opportunities. Front Public Heal, 5, 2017, 327.
75. Vickers, A.J., Whose data set is it anyway? Sharing raw data from randomized trials. Trials, 7, 2006, 15.
76. Sommer, J., The delay in sharing research data is costing lives. Nat Med, 16, 2010, 744.
77. Roelofs, E., et al. International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining. Radiother Oncol 110 (2014), 370–374.
78. Naqa, I.E., et al. Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose-volume outcome relationships. Phys Med Biol 51 (2006), 5719–5735.
79. Kalpathy-Cramer, J., et al. Development of a software for quantitative evaluation radiotherapy target and organ-at-risk segmentation comparison. J Digit Imaging 27 (2014), 108–119.
80. Zhang, L., et al. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 42 (2015), 1341–1353.
81. Munoz, L., Ziebell, A., Morton, J., Bhat, M., An open source solution for an in-house built dynamic platform for the validation of stereotactic ablative body radiotherapy for VMAT and IMRT. Australas Phys Eng Sci Med 39 (2016), 957–964.
82. MICCAI/M.D., Anderson Cancer Center Head and Neck Quantitative Imaging Working Group. Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. Sci Data, 4, 2017, 170077.
83. Ger, R.B., et al. A multi-institutional comparison of dynamic contrast-enhanced magnetic resonance imaging parameter calculations. Sci Rep, 7, 2017, 11185.
84. Kalpathy-Cramer, J., Freymann, J.B., Kirby, J.S., Kinahan, P.E., Prior, F.W., Quantitative imaging network: data sharing and competitive algorithm validation leveraging the cancer imaging archive. Transl Oncol 7 (2014), 147–152.
85. Manrai, A.K., et al. Genetic misdiagnoses and the potential for health disparities. N Engl J Med 375 (2016), 655–665.
86. Khozin, S., Kim, G., Pazdur, R., Regulatory watch: From big data to smart data: FDA's INFORMED initiative. Nat Rev Drug Discovery, 16, 2017, 306.
87. NCI announces oncology data science fellowship. (2017). Available at: https://astroblog.weebly.com/blog/nci-announces-oncology-data-science-fellowship.
88. Smith, B.D., et al. The future of radiation oncology in the United States from 2010 to 2020: will supply keep pace with demand?. J Clin Oncol 28 (2010), 5160–5165.
89. Pan, H.Y., et al. Supply and demand for radiation oncology in the United States: updated projections for 2015 to 2025. Int J Radiat Oncol Biol Phys 96 (2016), 493–500.