Artificial Intelligence in Radiation Oncology

Overview

Journal Nat Rev Clin Oncol

Specialty Oncology

Date 2020 Aug 27

PMID 32843739

Citations 120

Authors

Elizabeth Huynh

Ahmed Hosny

Christian Guthier

Danielle S Bitterman

Steven F Petit

Daphne A Haas-Kogan

Benjamin Kann

Hugo J W L Aerts

Raymond H Mak

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex patterns in medical data and provide a quantitative, rather than purely qualitative, assessment of clinical conditions. Accordingly, AI could have particularly transformative applications in radiation oncology given the multifaceted and highly technical nature of this field of medicine with a heavy reliance on digital data processing and computer software. Indeed, AI has the potential to improve the accuracy, precision, efficiency and overall quality of radiation therapy for patients with cancer. In this Perspective, we first provide a general description of AI methods, followed by a high-level overview of the radiation therapy workflow with discussion of the implications that AI is likely to have on each step of this process. Finally, we describe the challenges associated with the clinical development and implementation of AI platforms in radiation oncology and provide our perspective on how these platforms might change the roles of radiotherapy medical professionals.

Citing Articles

Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.

DAmiano A, Cheunkarndee T, Azoba C, Chen K, Mak R, Perni S Cancer Med. 2025; 14(5):e70728.

PMID: 40059400 PMC: 11891267. DOI: 10.1002/cam4.70728.

Continuous multimodal data supply chain and expandable clinical decision support for oncology.

Chang J, Kim H, Baek E, Choi J, Lim J, Kim J NPJ Digit Med. 2025; 8(1):128.

PMID: 40016534 PMC: 11868524. DOI: 10.1038/s41746-025-01508-2.

A Conceptual Framework for Applying Ethical Principles of AI to Medical Practice.

Jha D, Durak G, Sharma V, Keles E, Cicek V, Zhang Z Bioengineering (Basel). 2025; 12(2).

PMID: 40001699 PMC: 11851997. DOI: 10.3390/bioengineering12020180.

Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review.

Chen Z, Hao J, Sun H, Li M, Zhang Y, Qian Q BMC Med Inform Decis Mak. 2025; 25(1):77.

PMID: 39948530 PMC: 11823091. DOI: 10.1186/s12911-025-02870-7.

Efficiency and Clinical Utility of AI-Assisted Radiotherapy Planning Using RatoGuide for Oropharyngeal Cancer: A Case Report.

Ishikawa Y, Ito K, Teramura S, Yamada T Cureus. 2025; 17(2):e78388.

PMID: 39906643 PMC: 11793990. DOI: 10.7759/cureus.78388.

References

Delaney G, Jacob S, Featherstone C, Barton M . The role of radiotherapy in cancer treatment: estimating optimal utilization from a review of evidence-based clinical guidelines. Cancer. 2005; 104(6):1129-37. DOI: 10.1002/cncr.21324. View

Pan H, Haffty B, Falit B, Buchholz T, Wilson L, Hahn S . Supply and Demand for Radiation Oncology in the United States: Updated Projections for 2015 to 2025. Int J Radiat Oncol Biol Phys. 2016; 96(3):493-500. DOI: 10.1016/j.ijrobp.2016.02.064. View

Miller K, Siegel R, Lin C, Mariotto A, Kramer J, Rowland J . Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin. 2016; 66(4):271-89. DOI: 10.3322/caac.21349. View

Atun R, Jaffray D, Barton M, Bray F, Baumann M, Vikram B . Expanding global access to radiotherapy. Lancet Oncol. 2015; 16(10):1153-86. DOI: 10.1016/S1470-2045(15)00222-3. View

Grover S, Xu M, Yeager A, Rosman L, Groen R, Chackungal S . A systematic review of radiotherapy capacity in low- and middle-income countries. Front Oncol. 2015; 4:380. PMC: 4302829. DOI: 10.3389/fonc.2014.00380. View

Kresl J, Drummond R . A historical perspective of the radiation oncology workforce and ongoing initiatives to affect recruitment and retention. J Am Coll Radiol. 2007; 1(9):641-8. DOI: 10.1016/j.jacr.2004.05.001. View

Peters L, OSullivan B, Giralt J, Fitzgerald T, Trotti A, Bernier J . Critical impact of radiotherapy protocol compliance and quality in the treatment of advanced head and neck cancer: results from TROG 02.02. J Clin Oncol. 2010; 28(18):2996-3001. DOI: 10.1200/JCO.2009.27.4498. View

Brade A, Wenz F, Koppe F, Lievens Y, Antonio B, Iscoe N . Radiation Therapy Quality Assurance (RTQA) of Concurrent Chemoradiation Therapy for Locally Advanced Non-Small Cell Lung Cancer in the PROCLAIM Phase 3 Trial. Int J Radiat Oncol Biol Phys. 2018; 101(4):927-934. DOI: 10.1016/j.ijrobp.2018.04.015. View

Kalet I, Paluszynski W . Knowledge-based computer systems for radiotherapy planning. Am J Clin Oncol. 1990; 13(4):344-51. DOI: 10.1097/00000421-199008000-00015. View

10.

Laramore G, Altschuler M, Banks G, Kalet I, Pajak T, Schultheiss T . Applications of data bases and AI/expert systems in radiation therapy. Am J Clin Oncol. 1988; 11(3):387-93. DOI: 10.1097/00000421-198806000-00015. View

11.

Sanders G, LYONS E . The potential use of expert systems to enable physicians to order more cost-effective diagnostic imaging examinations. J Digit Imaging. 1991; 4(2):112-22. DOI: 10.1007/BF03170419. View

12.

Hosny A, Parmar C, Quackenbush J, Schwartz L, Aerts H . Artificial intelligence in radiology. Nat Rev Cancer. 2018; 18(8):500-510. PMC: 6268174. DOI: 10.1038/s41568-018-0016-5. View

13.

Fukushima K . Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern. 1980; 36(4):193-202. DOI: 10.1007/BF00344251. View

14.

Feng M, Valdes G, Dixit N, Solberg T . Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs. Front Oncol. 2018; 8:110. PMC: 5913324. DOI: 10.3389/fonc.2018.00110. View

15.

Kann B, Aneja S, Loganadane G, Kelly J, Smith S, Decker R . Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks. Sci Rep. 2018; 8(1):14036. PMC: 6145900. DOI: 10.1038/s41598-018-32441-y. View

16.

Savova G, Tseytlin E, Finan S, Castine M, Miller T, Medvedeva O . DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records. Cancer Res. 2017; 77(21):e115-e118. PMC: 5690492. DOI: 10.1158/0008-5472.CAN-17-0615. View

17.

Hong J, Niedzwiecki D, Palta M, Tenenbaum J . Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm. JCO Clin Cancer Inform. 2019; 2:1-11. DOI: 10.1200/CCI.18.00037. View

18.

Oberije C, De Ruysscher D, Houben R, van de Heuvel M, Uyterlinde W, Deasy J . A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients. Int J Radiat Oncol Biol Phys. 2015; 92(4):935-44. PMC: 4786012. DOI: 10.1016/j.ijrobp.2015.02.048. View

19.

Jochems A, Deist T, El Naqa I, Kessler M, Mayo C, Reeves J . Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries. Int J Radiat Oncol Biol Phys. 2017; 99(2):344-352. PMC: 5575360. DOI: 10.1016/j.ijrobp.2017.04.021. View

20.

Deist T, Dankers F, Valdes G, Wijsman R, Hsu I, Oberije C . Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys. 2018; 45(7):3449-3459. PMC: 6095141. DOI: 10.1002/mp.12967. View