Literature DB >> 32728877

Artificial intelligence in radiotherapy: a technological review.

Ke Sheng1.   

Abstract

Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.

Entities:  

Keywords:  artificial intelligence; medical imaging; outcome prediction; quality assurance; radiation therapy; treatment planning

Mesh:

Year:  2020        PMID: 32728877     DOI: 10.1007/s11684-020-0761-1

Source DB:  PubMed          Journal:  Front Med        ISSN: 2095-0217            Impact factor:   4.592


  5 in total

1.  Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy.

Authors:  Christian Jamtheim Gustafsson; Michael Lempart; Johan Swärd; Emilia Persson; Tufve Nyholm; Camilla Thellenberg Karlsson; Jonas Scherman
Journal:  J Appl Clin Med Phys       Date:  2021-10-08       Impact factor: 2.102

Review 2.  Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT.

Authors:  Matthijs Ferdinand Kruis
Journal:  J Appl Clin Med Phys       Date:  2021-11-07       Impact factor: 2.102

3.  Attention-aware 3D U-Net convolutional neural network for knowledge-based planning 3D dose distribution prediction of head-and-neck cancer.

Authors:  Alexander F I Osman; Nissren M Tamam
Journal:  J Appl Clin Med Phys       Date:  2022-05-09       Impact factor: 2.243

4.  Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.

Authors:  Ahmed Hosny; Danielle S Bitterman; Christian V Guthier; Jack M Qian; Hannah Roberts; Subha Perni; Anurag Saraf; Luke C Peng; Itai Pashtan; Zezhong Ye; Benjamin H Kann; David E Kozono; David Christiani; Paul J Catalano; Hugo J W L Aerts; Raymond H Mak
Journal:  Lancet Digit Health       Date:  2022-09

Review 5.  Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy.

Authors:  Xi Liu; Kai-Wen Li; Ruijie Yang; Li-Sheng Geng
Journal:  Front Oncol       Date:  2021-07-08       Impact factor: 6.244

  5 in total

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