Literature DB >> 30527225

The application of artificial intelligence in the IMRT planning process for head and neck cancer.

Vasant Kearney1, Jason W Chan1, Gilmer Valdes1, Timothy D Solberg1, Sue S Yom2.   

Abstract

Artificial intelligence (AI) is beginning to transform IMRT treatment planning for head and neck patients. However, the complexity and novelty of AI algorithms make them susceptible to misuse by researchers and clinicians. Understanding nuances of new technologies could serve to mitigate potential clinical implementation pitfalls. This article is intended to facilitate integration of AI into the radiotherapy clinic by providing an overview of AI algorithms, including support vector machines (SVMs), random forests (RF), gradient boosting (GB), and several variations of deep learning. This document describes current AI algorithms that have been applied to head and neck IMRT planning and identifies rapidly growing branches of AI in industry that have potential applications to head and neck cancer patients receiving IMRT. AI algorithms have great clinical potential if used correctly but can also cause harm if misused, so it is important to raise the level of AI competence within radiation oncology so that the benefits can be realized in a controlled and safe manner.
Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Automated treatment planning; Convolutional neural networks; Deep learning; Head and neck; Intensity modulated radiation therapy; Machine learning; Predictive medicine; Radiation oncology; Treatment planning

Mesh:

Year:  2018        PMID: 30527225     DOI: 10.1016/j.oraloncology.2018.10.026

Source DB:  PubMed          Journal:  Oral Oncol        ISSN: 1368-8375            Impact factor:   5.337


  10 in total

1.  Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.

Authors:  Atta-Ur Rahman; Abdullah Alqahtani; Nahier Aldhafferi; Muhammad Umar Nasir; Muhammad Farhan Khan; Muhammad Adnan Khan; Amir Mosavi
Journal:  Sensors (Basel)       Date:  2022-05-18       Impact factor: 3.847

2.  Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks.

Authors:  Vasant Kearney; Benjamin P Ziemer; Alan Perry; Tianqi Wang; Jason W Chan; Lijun Ma; Olivier Morin; Sue S Yom; Timothy D Solberg
Journal:  Radiol Artif Intell       Date:  2020-03-25

Review 3.  Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.

Authors:  Antonio Jesús Banegas-Luna; Jorge Peña-García; Adrian Iftene; Fiorella Guadagni; Patrizia Ferroni; Noemi Scarpato; Fabio Massimo Zanzotto; Andrés Bueno-Crespo; Horacio Pérez-Sánchez
Journal:  Int J Mol Sci       Date:  2021-04-22       Impact factor: 5.923

4.  Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region.

Authors:  Patrik Sibolt; Lina M Andersson; Lucie Calmels; David Sjöström; Ulf Bjelkengren; Poul Geertsen; Claus F Behrens
Journal:  Phys Imaging Radiat Oncol       Date:  2020-12-18

5.  Barriers and facilitators to the adoption of artificial intelligence in radiation oncology: A New Zealand study.

Authors:  Koki Victor Mugabe
Journal:  Tech Innov Patient Support Radiat Oncol       Date:  2021-04-21

Review 6.  A Review of PRESAGE Radiochromic Polymer and the Compositions for Application in Radiotherapy Dosimetry.

Authors:  Muhammad Zamir Mohyedin; Hafiz Mohd Zin; Mohd Zulfadli Adenan; Ahmad Taufek Abdul Rahman
Journal:  Polymers (Basel)       Date:  2022-07-16       Impact factor: 4.967

7.  Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer.

Authors:  E Tryggestad; A Anand; C Beltran; J Brooks; J Cimmiyotti; N Grimaldi; T Hodge; A Hunzeker; J J Lucido; N N Laack; R Momoh; D J Moseley; S H Patel; A Ridgway; S Seetamsetty; S Shiraishi; L Undahl; R L Foote
Journal:  Front Oncol       Date:  2022-08-29       Impact factor: 5.738

Review 8.  Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review.

Authors:  Sanjeev B Khanagar; Sachin Naik; Abdulaziz Abdullah Al Kheraif; Satish Vishwanathaiah; Prabhadevi C Maganur; Yaser Alhazmi; Shazia Mushtaq; Sachin C Sarode; Gargi S Sarode; Alessio Zanza; Luca Testarelli; Shankargouda Patil
Journal:  Diagnostics (Basel)       Date:  2021-05-31

9.  Clinical Implementation of Automated Treatment Planning for Rectum Intensity-Modulated Radiotherapy Using Voxel-Based Dose Prediction and Post-Optimization Strategies.

Authors:  Yang Zhong; Lei Yu; Jun Zhao; Yingtao Fang; Yanju Yang; Zhiqiang Wu; Jiazhou Wang; Weigang Hu
Journal:  Front Oncol       Date:  2021-06-24       Impact factor: 6.244

Review 10.  Emerging radiotherapy technologies and trends in nasopharyngeal cancer.

Authors:  Michelle Tseng; Francis Ho; Yiat Horng Leong; Lea Choung Wong; Ivan Wk Tham; Timothy Cheo; Anne Wm Lee
Journal:  Cancer Commun (Lond)       Date:  2020-08-03
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.