Evaluation of two independent dose prediction methods to personalize the automated radiotherapy planning process for prostate cancer-Reference-Cited by-同舟云学术

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Evaluation of two independent dose prediction methods to personalize the automated radiotherapy planning process for prostate cancer

Published:2022-01 Issue: Volume:21 Page:24-29
ISSN:2405-6316
Container-title:Physics and Imaging in Radiation Oncology
language:en
Short-container-title:Physics and Imaging in Radiation Oncology

Author:

Kusters Martijn,Miki Kentaro,Bouwmans Liza,Bzdusek Karl,van Kollenburg Peter,Smeenk Robert Jan,Monshouwer René,Nagata Yasushi

Funder

Philips

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging,Radiation

Reference20 articles.

1. Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution;Scaggion;Phys Med,2018

2. Improved planning time and plan quality through multicriteria optimization for intensity modulated radiotherapy;Craft;Int J Radiat Oncol Biol Phys,2012

3. Evaluation of a knowledge-based planning solution for head and neck cancer;Tol;Int J Radiat Oncol Biol Phys,2015

4. Dosimetric comparison of RapidPlan and manually optimized plans in volumetric modulated arc therapy for prostate cancer;Kubo;Phys Med,2017

5. Personalized treatment planning automation in prostate cancer radiation oncology: a comprehensive dosimetric study;Cilla;Front Oncol,2021

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A deep learning based dynamic arc radiotherapy photon dose engine trained on Monte Carlo dose distributions;Physics and Imaging in Radiation Oncology;2024-04

2. Knowledge-based versus deep learning based treatment planning for breast radiotherapy;Physics and Imaging in Radiation Oncology;2024-01

3. A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer;Physics and Imaging in Radiation Oncology;2023-10

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