Literature DB >> 33914614

Artificial intelligence in brachytherapy: a summary of recent developments.

Susovan Banerjee1, Shikha Goyal2, Saumyaranjan Mishra1, Deepak Gupta1, Shyam Singh Bisht1, Venketesan K1, Kushal Narang1, Tejinder Kataria1.   

Abstract

Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects of medicine, including radiation oncology. Ample evidence from recent publications explores its utility and future use in external beam radiotherapy. However, the discussion on its role in brachytherapy is sparse. This article summarizes available current literature and discusses potential uses of AI in brachytherapy, including future directions. AI has been applied for brachytherapy procedures during almost all steps, starting from decision-making till treatment completion. AI use has led to improvement in efficiency and accuracy by reducing the human errors and saving time in certain aspects. Apart from direct use in brachytherapy, AI also contributes to contemporary advancements in radiology and associated sciences that can affect brachytherapy decisions and treatment. There is a renewal of interest in brachytherapy as a technique in recent years, contributed largely by the understanding that contemporary advances such as intensity modulated radiotherapy and stereotactic external beam radiotherapy cannot match the geometric gains and conformality of brachytherapy, and the integrated efforts of international brachytherapy societies to promote brachytherapy training and awareness. Use of AI technologies may consolidate it further by reducing human effort and time. Prospective validation over larger studies and incorporation of AI technologies for a larger patient population would help improve the efficiency and acceptance of brachytherapy. The enthusiasm favoring AI needs to be balanced against the short duration and quantum of experience with AI in limited patient subsets, need for constant learning and re-learning to train the AI algorithms, and the inevitability of humans having to take responsibility for the correctness and safety of treatments.

Entities:  

Mesh:

Year:  2021        PMID: 33914614      PMCID: PMC8173686          DOI: 10.1259/bjr.20200842

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.629


  49 in total

Review 1.  Interpretation of observational studies.

Authors:  P Jepsen; S P Johnsen; M W Gillman; H T Sørensen
Journal:  Heart       Date:  2004-08       Impact factor: 5.994

2.  Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis.

Authors:  Gilmer Valdes; Albert J Chang; Yannet Interian; Kenton Owen; Shane T Jensen; Lyle H Ungar; Adam Cunha; Timothy D Solberg; I-Chow Hsu
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-03-13       Impact factor: 7.038

3.  Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy.

Authors:  Andrea Pella; Raffaella Cambria; Marco Riboldi; Barbara Alicja Jereczek-Fossa; Cristiana Fodor; Dario Zerini; Ahmad Esmaili Torshabi; Federica Cattani; Cristina Garibaldi; Guido Pedroli; Guido Baroni; Roberto Orecchia
Journal:  Med Phys       Date:  2011-06       Impact factor: 4.071

Review 4.  Muscle Segmentation for Orthopedic Interventions.

Authors:  Naoki Kamiya
Journal:  Adv Exp Med Biol       Date:  2018       Impact factor: 2.622

5.  Augmented Bladder Tumor Detection Using Deep Learning.

Authors:  Eugene Shkolyar; Xiao Jia; Timothy C Chang; Dharati Trivedi; Kathleen E Mach; Max Q-H Meng; Lei Xing; Joseph C Liao
Journal:  Eur Urol       Date:  2019-09-17       Impact factor: 20.096

6.  Deep-learning assisted automatic digitization of interstitial needles in 3D CT image based high dose-rate brachytherapy of gynecological cancer.

Authors:  Hyunuk Jung; Chenyang Shen; Yesenia Gonzalez; Kevin Albuquerque; Xun Jia
Journal:  Phys Med Biol       Date:  2019-10-23       Impact factor: 3.609

7.  Patient selection for accelerated partial-breast irradiation (APBI) after breast-conserving surgery: recommendations of the Groupe Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) breast cancer working group based on clinical evidence (2009).

Authors:  Csaba Polgár; Erik Van Limbergen; Richard Pötter; György Kovács; Alfredo Polo; Jaroslaw Lyczek; Guido Hildebrandt; Peter Niehoff; Jose Luis Guinot; Ferran Guedea; Bengt Johansson; Oliver J Ott; Tibor Major; Vratislav Strnad
Journal:  Radiother Oncol       Date:  2010-02-22       Impact factor: 6.280

8.  A tool to automatically analyze electromagnetic tracking data from high dose rate brachytherapy of breast cancer patients.

Authors:  Th I Götz; G Lahmer; V Strnad; Ch Bert; B Hensel; A M Tomé; E W Lang
Journal:  PLoS One       Date:  2017-09-21       Impact factor: 3.240

Review 9.  Applications and limitations of machine learning in radiation oncology.

Authors:  Daniel Jarrett; Eleanor Stride; Katherine Vallis; Mark J Gooding
Journal:  Br J Radiol       Date:  2019-06-05       Impact factor: 3.629

Review 10.  Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology.

Authors:  Rajit Rattan; Tejinder Kataria; Susovan Banerjee; Shikha Goyal; Deepak Gupta; Akshi Pandita; Shyam Bisht; Kushal Narang; Saumya Ranjan Mishra
Journal:  BJR Open       Date:  2019-05-13
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.