Machine learning‐based radiotherapy time prediction and treatment scheduling management
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Published:2023-08-17
Issue:9
Volume:24
Page:
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ISSN:1526-9914
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Container-title:Journal of Applied Clinical Medical Physics
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language:en
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Short-container-title:J Applied Clin Med Phys
Author:
Xie Lisiqi1,
Xu Dan1,
He Kangjian1,
Tian Xin2
Affiliation:
1. School of Information Science and Engineering Yunnan University Kunming China
2. Department of Radiation Oncology The Second Affiliated Hospital of Kunming Medical University Kunming China
Abstract
AbstractPurposeThe utility efficiency of medical devices is important, especially for countries such as China, which have a large population and shortage of medical care resources. Radiotherapy devices are among the most valuable and specialized equipment categories and carry enormous treatment loads. In this study, a novel method is proposed to improve the efficiency of a radiotherapy device (linac). Although scheduling management with accurate prediction of the entire treatment time included in each appointment, arrange a reasonable time duration for appointments and save time between patient shifts effectively. Tasks belonging to the treatment and non‐treatment groups can be assigned more flexibly based on the availability of time.Material and methodsData from 1665 patients, including patient positioning time (PT) and treatment time (TT), were collected in collaboration with the Radiotherapy Center of the Department of Oncology at the Second Affiliated Hospital of Kunming Medical University from November 2020 to August 2021. The features related to PT and TT were extracted and used to train the machine learning‐based model to predict PT and TT in independent patients. The prediction results were subsequently applied to a minute‐based scheduling tool.ConclusionArtificial intelligence is a promising approach to solve abstract problems with a specialized knowledge background. The results of this study show encouraging prediction outcomes in relation to effective scheduling management and could improve the efficiency of the linac. This successful trial broadens the meaning of medical data and potential future research directions in radiotherapy.
Funder
National Natural Science Foundation of China
Subject
Radiology, Nuclear Medicine and imaging,Instrumentation,Radiation
Reference22 articles.
1. Augmented reality‐guided positioning system for radiotherapy patients
2. Survey on the basic information of personnel and facilities of radiotherapy in Chinese mainland in 2019;Ye YJLZ;China Cancer,2020
3. Expanding global access to radiotherapy
4. Redionala cancercentrum i samverkan.Standardiserade vårdförlopp i cancervården (Eng: Standardized Patient Care Pathways for Cancer Care). [EB/OL]. Accessed May 12 2021.https://www.cancercentrum.se/samverkan/vara‐uppdrag/kunskapsstyrning/vardforlopp/