A Review of Artificial Intelligence in Brachytherapy

Overview

Journal ArXiv

Date 2024 Oct 14

PMID 39398213

Authors

Jingchu Chen

Richard L J Qiu

Tonghe Wang

Shadab Momin

Xiaofeng Yang

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in facilitating various aspects of brachytherapy. We analyze AI's role in making brachytherapy treatments more personalized, efficient, and effective. The applications are systematically categorized into seven categories: imaging, preplanning, treatment planning, applicator reconstruction, quality assurance, outcome prediction, and real-time monitoring. Each major category is further subdivided based on cancer type or specific tasks, with detailed summaries of models, data sizes, and results presented in corresponding tables. This review offers insights into the current advancements, challenges, and the impact of AI on treatment paradigms, encouraging further research to expand its clinical utility.

References

Ma J, He Y, Li F, Han L, You C, Wang B . Segment anything in medical images. Nat Commun. 2024; 15(1):654. PMC: 10803759. DOI: 10.1038/s41467-024-44824-z. View

Swamidas J, Kirisits C, De Brabandere M, Hellebust T, Siebert F, Tanderup K . Image registration, contour propagation and dose accumulation of external beam and brachytherapy in gynecological radiotherapy. Radiother Oncol. 2019; 143:1-11. DOI: 10.1016/j.radonc.2019.08.023. View

Zeng Q, Fu Y, Tian Z, Lei Y, Zhang Y, Wang T . Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy. Phys Med Biol. 2020; 65(13):135002. PMC: 7771987. DOI: 10.1088/1361-6560/ab8cd6. View

Zabihollahy F, Viswanathan A, Schmidt E, Lee J . Fully automated segmentation of clinical target volume in cervical cancer from magnetic resonance imaging with convolutional neural network. J Appl Clin Med Phys. 2022; 23(9):e13725. PMC: 9512359. DOI: 10.1002/acm2.13725. View

Demanes D, Rege S, RODRIQUEZ R, Schutz K, Altieri G, Wong T . The use and advantages of a multichannel vaginal cylinder in high-dose-rate brachytherapy. Int J Radiat Oncol Biol Phys. 1999; 44(1):211-9. DOI: 10.1016/s0360-3016(98)00453-2. View

Golshan M, Karimi D, Mahdavi S, Lobo J, Peacock M, Salcudean S . Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network. Phys Med Biol. 2019; 65(3):035016. DOI: 10.1088/1361-6560/ab64b5. View

Rodriguez Outeiral R, Gonzalez P, Schaake E, van der Heide U, Simoes R . Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy. Radiat Oncol. 2023; 18(1):91. PMC: 10227985. DOI: 10.1186/s13014-023-02283-8. View

Podgorsak A, Venkatesulu B, Abuhamad M, Harkenrider M, Solanki A, Roeske J . Dosimetric and workflow impact of synthetic-MRI use in prostate high-dose-rate brachytherapy. Brachytherapy. 2023; 22(5):686-696. DOI: 10.1016/j.brachy.2023.05.005. View

Gao Y, Gonzalez Y, Nwachukwu C, Albuquerque K, Jia X . Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning. Phys Med Biol. 2024; 69(9). PMC: 11023000. DOI: 10.1088/1361-6560/ad3880. View

10.

Ecker S, Zimmermann L, Heilemann G, Niatsetski Y, Schmid M, Sturdza A . Neural network-assisted automated image registration for MRI-guided adaptive brachytherapy in cervical cancer. Z Med Phys. 2022; 32(4):488-499. PMC: 9948828. DOI: 10.1016/j.zemedi.2022.04.002. View

11.

Lei Y, Wang T, Roper J, Jani A, Patel S, Curran W . Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN. Med Phys. 2021; 48(6):3055-3064. PMC: 11700376. DOI: 10.1002/mp.14895. View

12.

Karimi D, Zeng Q, Mathur P, Avinash A, Mahdavi S, Spadinger I . Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Med Image Anal. 2019; 57:186-196. DOI: 10.1016/j.media.2019.07.005. View

13.

Peters M, van Son M, Moerland M, Kerkmeijer L, Eppinga W, Meijer R . MRI-Guided Ultrafocal HDR Brachytherapy for Localized Prostate Cancer: Median 4-Year Results of a feasibility study. Int J Radiat Oncol Biol Phys. 2019; 104(5):1045-1053. DOI: 10.1016/j.ijrobp.2019.03.032. View

14.

Yusufaly T, Meyers S, Mell L, Moore K . Knowledge-Based Planning for Intact Cervical Cancer. Semin Radiat Oncol. 2020; 30(4):328-339. DOI: 10.1016/j.semradonc.2020.05.009. View

15.

Boussion N, Schick U, Dissaux G, Ollivier L, Goasduff G, Pradier O . A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy. J Contemp Brachytherapy. 2021; 13(5):541-548. PMC: 8565637. DOI: 10.5114/jcb.2021.109789. View

16.

Zhang Y, He X, Tian Z, Jeong J, Lei Y, Wang T . Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning. IEEE Trans Med Imaging. 2020; 39(7):2302-2315. PMC: 7370243. DOI: 10.1109/TMI.2020.2968770. View

17.

Grimm P, Billiet I, Bostwick D, Dicker A, Frank S, Immerzeel J . Comparative analysis of prostate-specific antigen free survival outcomes for patients with low, intermediate and high risk prostate cancer treatment by radical therapy. Results from the Prostate Cancer Results Study Group. BJU Int. 2012; 109 Suppl 1:22-9. DOI: 10.1111/j.1464-410X.2011.10827.x. View

18.

Yoganathan S, Paul S, Paloor S, Torfeh T, Halsnad Chandramouli S, Hammoud R . Automatic segmentation of magnetic resonance images for high-dose-rate cervical cancer brachytherapy using deep learning. Med Phys. 2022; 49(3):1571-1584. DOI: 10.1002/mp.15506. View

19.

Skowronek J . Current status of brachytherapy in cancer treatment - short overview. J Contemp Brachytherapy. 2018; 9(6):581-589. PMC: 5808003. DOI: 10.5114/jcb.2017.72607. View

20.

Rajpurkar P, Chen E, Banerjee O, Topol E . AI in health and medicine. Nat Med. 2022; 28(1):31-38. DOI: 10.1038/s41591-021-01614-0. View