Abstract
Artificial intelligence (AI) is emerging as a technology with the power to transform
established industries, and with applications from automated manufacturing to advertising
and facial recognition to fully autonomous transportation. Advances in each of these
domains have led some to call AI the “fourth” industrial revolution [
]. In healthcare, AI is emerging as both a productive and disruptive force across many
disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties
largely built around the processing and complex interpretation of medical images,
where the role of AI is increasingly seen as both a boon and a threat. In Radiation
Oncology as well, AI seems poised to reshape the specialty in significant ways, though
the impact of AI has been relatively limited at present, and may rightly seem more
distant to many, given the predominantly interpersonal and complex interventional
nature of the specialty. In this overview, we will explore the current state and anticipated
future impact of AI on Radiation Oncology, in detail, focusing on key topics from
multiple stakeholder perspectives, as well as the role our specialty may play in helping
to shape the future of AI within the larger spectrum of medicine.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Radiotherapy and OncologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- The fourth industrial revolution is upon us.The Washington Post, 2017
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)
- Clinical decision support of radiotherapy treatment planning: a data-driven machine learning strategy for patient-specific dosimetric decision making.Radiother Oncol. 2017; 125: 392-397https://doi.org/10.1016/j.radonc.2017.10.014
- The cutting edge: delineating contours with Deep.Learning. 2017;
Oncora Medical. Available at: https://oncoramedical.com/. (Accessed: 1st November 2017)
- Big Data and machine learning in radiation oncology: state of the art and future prospects.Cancer Lett. 2016; 382: 110-117
- Lost in thought—The limits of the human mind and the future of medicine.N Engl J Med. 2017; 377: 1209-1211
- Machine learning and prediction in medicine – beyond the peak of inflated expectations.N Engl J Med. 2017; 376: 2507-2509
- Interpretation of observational studies.Heart. 2004; 90: 956-960
- Unintended consequences of machine learning in medicine.JAMA. 2017; 318: 517-518
- Automatic tumor segmentation using knowledge-based techniques.IEEE Trans Med Imaging. 1998; 17: 187-201
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)
Kamnitsas, K. et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. (2016).
- Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer.Radiother Oncol. 2014; 112: 321-325
- Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.Med Phys. 2017; 44: 547-557
- Comparison of automated atlas-based segmentation software for postoperative prostate cancer radiotherapy.Front Oncol. 2016; 6: 178
- 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. 2013; 87: 176-181
- Using overlap volume histogram and IMRT plan data to guide and automate VMAT planning: a head-and-neck case study.Med Phys. 2013; 40: 21714
- Experience-based quality control of clinical intensity-modulated radiotherapy planning.Int J Radiat Oncol Biol Phys. 2011; 81: 545-551
- Knowledge-based radiation therapy (KBRT) treatment planning versus planning by experts: validation of a KBRT algorithm for prostate cancer treatment planning.Radiat Oncol. 2015; 10: 111
- 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. 2017; 12: e0178034
- 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. 2017; 97: 164-172
- Performance of knowledge-based radiation therapy planning for the glioblastoma disease site.Int J Radiat Oncol. 2017; https://doi.org/10.1016/j.ijrobp.2017.07.012
- Evaluation of a knowledge-based planning solution for head and neck cancer.Int J Radiat Oncol Biol Phys. 2015; 91: 612-620
- An analysis of knowledge-based planning for stereotactic body radiation therapy of the spine.Pract Radiat Oncol. 2017; https://doi.org/10.1016/j.prro.2017.02.007
- A planning quality evaluation tool for prostate adaptive IMRT based on machine learning.Med Phys. 2011; 38: 719-726
- Predicting dose-volume histograms for organs-at-risk in IMRT planning.Med Phys. 2012; 39: 7446-7461
- Quantifying unnecessary normal tissue complication risks due to suboptimal planning: a secondary study of RTOG 0126.Int J Radiat Oncol Biol Phys. 2015; 92: 228-235
- 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. 2016; 95: 981-990
- 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. 2016; 118: 281-285
- Selection of patients for radiotherapy with protons aiming at reduction of side effects: the model-based approach.Radiother Oncol. 2013; 107: 267-273
- Predicting patient-specific dosimetric benefits of proton therapy for skull-base tumors using a geometric knowledge-based method.Int J Radiat Oncol Biol Phys. 2017; 97: 1087-1094
- Datamining approaches for modeling tumor control probability.Acta Oncol. 2010; 49: 1363-1373
- Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors.Int J Radiat Oncol Biol Phys. 2006; 64: 1275-1286
- 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. 2014; 112: 37-43
- Dosimetric correlates for acute esophagitis in patients treated with radiotherapy for lung carcinoma.Int J Radiat Oncol Biol Phys. 2004; 58: 1106-1113
- Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters.Int J Radiat Oncol Biol Phys. 2006; 65: 112-124
- CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer.Radiother Oncol. 2016; 120: 258-266
- Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.Clin Transl Radiat Oncol. 2017; 4: 24-31
OncoSpace. Available at: https://oncospace.radonc.jhmi.edu/. (Accessed: 1st December 2017)
Flatiron Health. Available at: https://flatiron.com/. (Accessed: 1st November 2017)
- ‘Rapid Learning health care in oncology’ – an approach towards decision support systems enabling customised radiotherapy.Radiother Oncol. 2013; 109: 159-164
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.
Dekker, A. Radiation Oncology Ontology. Available at: http://bioportal.bioontology.org/ontologies/ROO.
- Radiation oncology needs to adopt a comprehensive standard for data transfer: the case for HL7 FHIR.Int J Radiat Oncol Biol Phys. 2017; 99: 1073-1075
- Representing knowledge consistently across health systems.Yearb Med Inform. 2017; 26: 139-147
- A mathematical framework for virtual IMRT QA using machine learning.Med Phys. 2016; 43: 4323
- IMRT QA using machine learning: a multi-institutional validation.J Appl Clin Med Phys. 2017; 18: 279-284
- Use of TrueBeam developer mode for imaging QA.J Appl Clin Med Phys. 2015; 16: 322-333
- Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.Ann N Y Acad Sci. 2017; 1387: 84-94
- Improving linear accelerator service response with a real- time electronic event reporting system.J Appl Clin Med Phys. 2014; 15: 4807
- Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.Int J Med Inform. 2017; 102: 71-79
- Online adaptive radiation therapy.Int J Radiat Oncol Biol Phys. 2017; 99: 994-1003
- Deskilling and adaptation among primary care physicians using two work innovations.Health Care Manage Rev. 2011; 36: 338-348
- Benefits of adaptive radiation therapy in lung cancer as a function of replanning frequency.Med Phys. 2016; 43: 1787
- Clinical outcomes among patients with head and neck cancer treated by intensity-modulated radiotherapy with and without adaptive replanning.Head Neck. 2014; 36: 1541-1546
- Artificial intelligence will reduce the need for clinical medical physicists.J. Appl Clin Med Phys. 2018; 19: 6-9
- Care for patients, not for charts: a future for clinical medical physics.Int J Radiat Oncol Biol Phys. 2018; 100: 21-22
- Half of hospitals to adopt artificial intelligence within 5 years.Healthcare IT News, 2017
- Data sharing: guard the privacy of donors.Nature. 2017; 548: 281
- Patient privacy in the era of big data.Balkan Med J. 2017; https://doi.org/10.4274/balkanmedj.2017.0966
- Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasets.Radiother Oncol. 2014; 113: 303-309
- 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. 2016; 121: 459-467
- Data re-identification: prioritize privacy.Science. 2013; 339: 1032
- The effect of the general data protection regulation on medical research.J Med Internet Res. 2017; 19: e47
European Society of Radiology (ESR). The new EU General Data Protection Regulation: what the radiologist should know. Insights Imaging 2017;8:295–299.
- NHS illegally handed Google firm 1.6m patient records, UK data watchdog finds.The Telegraph, 2017
- 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. 2011; 11: 178
- Monte Carlo characterization of target doses in stereotactic body radiation therapy (SBRT).Acta Oncol. 2006; 45: 989-994
- 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. 2014; 88: 1108-1113
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.
- Knowledge sharing as a social dilemma in pharmaceutical innovation.Food Drug Law J. 2016; 71: 673-709
- Advantages of a truly open-access data-sharing model.N Engl J Med. 2017; 376: 1178-1181
- Data sharing: convert challenges into opportunities.Front Public Heal. 2017; 5: 327
- Whose data set is it anyway? Sharing raw data from randomized trials.Trials. 2006; 7: 15
- The delay in sharing research data is costing lives.Nat Med. 2010; 16: 744
- International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining.Radiother Oncol. 2014; 110: 370-374
- Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose-volume outcome relationships.Phys Med Biol. 2006; 51: 5719-5735
- Development of a software for quantitative evaluation radiotherapy target and organ-at-risk segmentation comparison.J Digit Imaging. 2014; 27: 108-119
- IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.Med Phys. 2015; 42: 1341-1353
- 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. 2016; 39: 957-964
- Anderson Cancer Center Head and Neck Quantitative Imaging Working Group. Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges.Sci Data. 2017; 4: 170077
- A multi-institutional comparison of dynamic contrast-enhanced magnetic resonance imaging parameter calculations.Sci Rep. 2017; 7: 11185
- Quantitative imaging network: data sharing and competitive algorithm validation leveraging the cancer imaging archive.Transl Oncol. 2014; 7: 147-152
- Genetic misdiagnoses and the potential for health disparities.N Engl J Med. 2016; 375: 655-665
- Regulatory watch: From big data to smart data: FDA’s INFORMED initiative.Nat Rev Drug Discovery. 2017; 16: 306
NCI announces oncology data science fellowship. (2017). Available at: https://astroblog.weebly.com/blog/nci-announces-oncology-data-science-fellowship.
- The future of radiation oncology in the United States from 2010 to 2020: will supply keep pace with demand?.J Clin Oncol. 2010; 28: 5160-5165
- Supply and demand for radiation oncology in the United States: updated projections for 2015 to 2025.Int J Radiat Oncol Biol Phys. 2016; 96: 493-500
Article info
Publication history
Published online: June 12, 2018
Accepted:
May 30,
2018
Received in revised form:
May 29,
2018
Received:
March 7,
2018
Identification
Copyright
Published by Elsevier B.V.
