Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
Source abstract
Abstract
In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Radiomics and Machine Learning in Medical Imaging · Advanced Radiotherapy Techniques · Lung Cancer Diagnosis and Treatment
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
Medical Physics Specialization School, Alma Mater Studiorum, University of Bologna, 40138 Bologna, Italy
Istituto Nazionale di Fisica Nucleare (INFN) Sezione di Roma 1, 00185 Roma, Italy
Department of Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
Department of Experimental, Diagnostic and Specialty Medicine, Alma Mater Studiorum, University of Bologna, 40138 Bologna, Italy
References (100+)
Show source-provided references
1. Automation in intensity modulated radiotherapy treatment planning—A review of recent innovations10.1259/bjr.20180270 ↗
2. Artificial intelligence and machine learning for medical imaging: A technology review10.1016/j.ejmp.2021.04.016 ↗
3. Basic of machine learning and deep learning in imaging for medical physicists10.1016/j.ejmp.2021.03.026 ↗
4. Regulating Black-Box Medicine10.36644/mlr.116.3.regulating ↗
5. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews10.1136/bmj.n71 ↗
6. Structure and Processes of Existing Practice in Radiotherapy Peer Review: A Systematic Review of the Literature10.1016/j.clon.2020.10.017 ↗
7. Artificial Intelligence in radiotherapy: State of the art and future directions10.1007/s12032-020-01374-w ↗
8. Automated IMRT planning in Pinnacle: A study in head-and-neck cancer10.1007/s00066-017-1187-9 ↗
9. Marazzi, F., Tagliaferri, L., Masiello, V., Moschella, F., Colloca, G.F., Corvari, B., Sanchez, A.M., Capocchiano, N.D., Pastorino, R., and Iacomini, C. (2021). GENERATOR Breast DataMart-The Novel Breast Cancer Data Discovery System for Research and Monitoring: Preliminary Results and Future Perspectives. J. Pers. Med., 11.10.3390/jpm11020065 ↗
10. Implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning10.1111/1754-9485.13287 ↗
11. Galofaro, E., Malizia, C., Ammendolia, I., Galuppi, A., Guido, A., Ntreta, M., Siepe, G., Tolento, G., Veraldi, A., and Scirocco, E. (2021). COVID-19 Pandemic-Adapted Radiotherapy Guidelines: Are They Really Followed?. Curr. Oncol., 28.10.3390/curroncol28050288 ↗
12. Learning deep similarity metric for 3D MR-TRUS image registration10.1007/s11548-018-1875-7 ↗
13. Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity10.1007/978-3-030-00919-9_7 ↗
14. Deformable image registration in radiation therapy10.3857/roj.2017.00325 ↗
15. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 13210.1002/mp.12256 ↗
16. Lasso logistic regression to derive workflow-specific algorithm performance requirements as demonstrated for head and neck cancer deformable image registration in adaptive radiation therapy10.1088/1361-6560/ab9fc8 ↗
17. Deformable image registration based automatic CT-to-CT contour propagation for head and neck adaptive radiotherapy in the routine clinical setting10.1118/1.4901409 ↗
18. Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning10.1002/mp.14755 ↗
19. Accuracy quantification of a deformable image registration tool applied in a clinical setting10.1120/jacmp.v15i1.4564 ↗
20. Validation of Varian’s SmartAdapt® deformable image registration algorithm for clinical application10.1186/s13014-015-0372-1 ↗
21. Benchmarking of five commercial deformable image registration algorithms for head and neck patients10.1120/jacmp.v17i3.5735 ↗
22. Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: A multi-institutional study10.1002/mp.12737 ↗
23. Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy10.1016/j.ijrobp.2018.06.048 ↗
24. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes10.1109/TMI.2018.2845918 ↗
25. DRINet for Medical Image Segmentation10.1109/TMI.2018.2835303 ↗
26. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks10.1002/mp.13147 ↗
27. Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images10.1002/mp.13553 ↗
28. Variations in the contouring of organs at risk: Test case from a patient with oropharyngeal cancer10.1016/j.ijrobp.2010.10.019 ↗
29. Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region10.1016/j.phro.2020.12.004 ↗
30. Automated contouring and planning pipeline for hippocampal-avoidant whole-brain radiotherapy10.1186/s13014-020-01689-y ↗
31. Deep learning-based automatic delineation of the hippocampus by MRI: Geometric and dosimetric evaluation10.1186/s13014-020-01724-y ↗
32. Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI10.1002/mp.14378 ↗
33. A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases10.3389/fonc.2021.638197 ↗
34. AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy10.1002/mp.13300 ↗
35. Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images10.1186/s13014-020-01617-0 ↗
36. Kim, N.A.-O., Chun, J., Chang, J.A.-O., Lee, C.G., Keum, K.C., and Kim, J.S. (2021). Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area. Cancers, 13.10.3390/cancers13040702 ↗
37. Strategies to improve deep learning-based salivary gland segmentation10.1186/s13014-020-01721-1 ↗
38. Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study10.2196/26151 ↗
39. Automated delineation of head and neck organs at risk using synthetic MRI-aided mask scoring regional convolutional neural network10.1002/mp.15146 ↗
40. Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi10.1093/jrr/rrz086 ↗
41. Training and Validation of Deep Learning-Based Auto-Segmentation Models for Lung Stereotactic Ablative Radiotherapy Using Retrospective Radiotherapy Planning Contours10.3389/fonc.2021.626499 ↗
42. Automated Quality Assurance of OAR Contouring for Lung Cancer Based on Segmentation With Deep Active Learning10.3389/fonc.2020.00986 ↗
43. A 2D-3D hybrid convolutional neural network for lung lobe auto-segmentation on standard slice thickness computed tomography of patients receiving radiotherapy10.1186/s12938-021-00932-1 ↗
44. Automatic contouring of normal tissues with deep learning for preclinical radiation studies10.1088/1361-6560/ac4da3 ↗
45. Generalization vs. Specificity: In Which Cases Should a Clinic Train its Own Segmentation Models?10.3389/fonc.2020.00675 ↗
46. Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks10.3389/fonc.2020.581347 ↗
47. Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer10.1016/j.radonc.2020.09.045 ↗
48. Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach10.1016/j.artmed.2018.07.001 ↗
49. An Artificial Intelligence-Based Full-Process Solution for Radiotherapy: A Proof of Concept Study on Rectal Cancer10.3389/fonc.2020.616721 ↗
50. Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy10.1186/s13014-020-01528-0 ↗
51. Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth
52. Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy10.1016/j.radonc.2021.02.040 ↗
53. Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer10.1002/acm2.13470 ↗
54. Varian ethos online adaptive radiotherapy for prostate cancer: Early results of contouring accuracy, treatment plan quality, and treatment time10.1002/acm2.13479 ↗
55. CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines10.1016/j.radonc.2015.07.041 ↗
56. Organs at risk in the brain and their dose-constraints in adults and in children: A radiation oncologist’s guide for delineation in everyday practice10.1016/j.radonc.2015.01.016 ↗
57. ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer10.1016/j.radonc.2014.11.030 ↗
58. Consideration of dose limits for organs at risk of thoracic radiotherapy: Atlas for lung, proximal bronchial tree, esophagus, spinal cord, ribs, and brachial plexus10.1016/j.ijrobp.2010.07.1977 ↗
59. Upper abdominal normal organ contouring guidelines and atlas: A Radiation Therapy Oncology Group consensus10.1016/j.prro.2013.06.004 ↗
60. Pelvic normal tissue contouring guidelines for radiation therapy: A Radiation Therapy Oncology Group consensus panel atlas10.1016/j.ijrobp.2012.01.023 ↗
61. ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer10.1016/j.radonc.2018.01.014 ↗
62. Delineation of the neck node levels for head and neck tumors: A 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines10.1016/j.radonc.2013.10.010 ↗
63. ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer, version 1.110.1016/j.radonc.2015.12.027 ↗
64. Consensus Guidelines and Contouring Atlas for Pelvic Node Delineation in Prostate and Pelvic Node Intensity Modulated Radiation Therapy10.1016/j.ijrobp.2015.03.021 ↗
65. RTOG GU Radiation Oncology Specialists Reach Consensus on Pelvic Lymph Node Volumes for High-Risk Prostate Cancer10.1016/j.ijrobp.2008.08.002 ↗
66. Jeong, H.A.-O., Ntolkeras, G., Alhilani, M.A.-O., Atefi, S.R., Zöllei, L., Fujimoto, K., Pourvaziri, A., Lev, M.H., Grant, P.E., and Bonmassar, G. (2021). Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model. PLoS ONE, 16.10.1371/journal.pone.0241682 ↗
67. Head and neck cancer patient images for determining auto-segmentation accuracy in T2-weighted magnetic resonance imaging through expert manual segmentations10.1002/mp.13942 ↗
68. Advances in Auto-Segmentation10.1016/j.semradonc.2019.02.001 ↗
69. Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy10.3389/fonc.2021.717039 ↗
70. Radiomics classifier to quantify automatic segmentation quality of cardiac sub-structures for radiotherapy treatment planning10.1016/j.ejmp.2021.05.009 ↗
71. Using Spatial Probability Maps to Highlight Potential Inaccuracies in Deep Learning-Based Contours: Facilitating Online Adaptive Radiation Therapy10.1016/j.adro.2021.100658 ↗
72. Investigating the potential of deep learning for patient-specific quality assurance of salivary gland contours using EORTC-1219-DAHANCA-29 clinical trial data10.1080/0284186X.2020.1863463 ↗
73. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy10.1088/1361-6560/aada6d ↗
74. Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: From Hounsfield unit to density calibration curve to deep learning10.1002/mp.14387 ↗
75. Radiotherapy planning using MRI10.1088/0031-9155/60/22/R323 ↗
76. MRI simulation for radiotherapy treatment planning10.1118/1.4758068 ↗
77. Real-Time Whole-Brain Radiation Therapy: A Single-Institution Experience10.1016/j.ijrobp.2017.12.282 ↗
78. MR-based synthetic CT generation using a deep convolutional neural network method10.1002/mp.12155 ↗
79. A preliminary study of using a deep convolution neural network to generate synthesized CT images based on CBCT for adaptive radiotherapy of nasopharyngeal carcinoma10.1088/1361-6560/ab2770 ↗
80. RealDRR—Rendering of realistic digitally reconstructed radiographs using locally trained image-to-image translation10.1016/j.radonc.2020.10.004 ↗
81. A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI10.1002/mp.14418 ↗
82. Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy10.1016/j.radonc.2020.09.029 ↗
83. Dai, X., Lei, Y., Tian, Z., Wang, T., Liu, T., Curran, W.J., and Yang, X. (2021, January 15–19). Deep learning-based volumetric image generation from projection imaging for prostate radiotherapy. Proceedings of the Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, Online.10.1117/12.2581053 ↗
84. Tong, F., Nakao, M., Wu, S., Nakamura, M., and Matsuda, T. (2020, January 20–24). X-ray2Shape: Reconstruction of 3D Liver Shape from a Single 2D Projection Image. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montréal, QC, Canada.10.1109/EMBC44109.2020.9176655 ↗
85. A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases10.1016/j.radonc.2020.10.018 ↗
86. A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning10.3389/fonc.2020.580919 ↗
87. Personalized automation of treatment planning in head-neck cancer: A step forward for quality in radiation therapy?10.1016/j.ejmp.2020.12.015 ↗
88. Artificial intelligence-based radiotherapy machine parameter optimization using reinforcement learning10.1002/mp.14544 ↗
89. Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study10.3389/fonc.2021.636529 ↗
90. Challenges in lung and heart avoidance for postmastectomy breast cancer radiotherapy: Is automated planning the answer?10.1016/j.meddos.2021.03.002 ↗
91. Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network
92. Feasibility of image quality improvement for high-speed CBCT imaging using deep convolutional neural network for image-guided radiotherapy in prostate cancer10.1016/j.ejmp.2020.10.012 ↗
93. Markerless Respiratory Tumor Motion Prediction Using an Adaptive Neuro-fuzzy Approach10.4103/jmss.JMSS_45_17 ↗
94. Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow10.1088/1361-6560/abb0f9 ↗
95. Real-time markerless tumour tracking with patient-specific deep learning using a personalised data generation strategy: Proof of concept by phantom study10.1259/bjr.20190420 ↗
96. A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer10.1016/j.phro.2020.04.002 ↗
97. Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future10.1177/1533033819873922 ↗
98. Strategies for effective physics plan and chart review in radiation therapy: Report of AAPM Task Group 27510.1002/mp.14030 ↗
99. Machine learning and modeling: Data, validation, communication challenges
100. Medical Physics Practice Guideline (MPPG) 11.a: Plan and chart review in external beam radiotherapy and brachytherapy10.1002/acm2.13366 ↗