{"title":"放射治疗中剂量带预测的可行性研究:预测计划剂量谱","authors":"","doi":"10.1016/j.radonc.2024.110593","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><div>The current deep learning-based dose prediction methods only predict one dose distribution. If the predicted dose is inaccurate, no additional options can be selected. To overcome this limitation, we propose a novel dose prediction method called “dose-band prediction,” which provides a spectrum of predicted dose distributions for planning and quality assurance (QA) purposes.</div></div><div><h3>Material and methods</h3><div>We utilized Upper/Lower-band losses in 3D neural networks to establish the Upper/Lower-band models (UBM/LBM). The maximum/minimum rational dose predicted in UBM/LBM defined the ideal dose spectrum for each voxel. We enrolled 104 nasopharyngeal carcinoma cases with tomotherapy (dataset 1), 54 cervical carcinoma cases with IMRT (dataset 2), and 37 cervical carcinoma cases with VMAT (dataset 3) in the study. Moreover, a dose band-based auto planning (Auto-plan<sub>dose-band</sub>) attempt was carried out in dataset 3, compared with the MSE model (Auto-plan<sub>MSE</sub>).</div></div><div><h3>Results</h3><div>The UBM/LBM doses tend to be higher/lower than the clinical dose, forming a predicted dose spectrum. The Middle-line dose represents the average of the Upper/Lower-band, which was consistent with the clinical dose. The mean differences of the planning target volumes (PTVs) and organs at risk (OARs) for the Upper-band, Middle-line, and Lower-band in Dataset 1 were 3.66 %, −0.40 %, and −4.48 % in Dataset 2, they were 2.40 %, −1.62 %, and −5.57 %; in Dataset 3, they were 2.18 %, −0.59 %, and −3.31 %. When PTVs meet prescription, the mean difference between Auto-plan<sub>dose-band</sub> and Auto-plan<sub>MSE</sub> in OARs was −2.67 %.</div></div><div><h3>Conclusion</h3><div>The dose-band prediction successfully predicted a spectrum of doses, making auto-planning and QA flexible and high quality.</div></div>","PeriodicalId":21041,"journal":{"name":"Radiotherapy and Oncology","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A feasibility study of dose-band prediction in radiation therapy: Predicting a spectrum of plan dose\",\"authors\":\"\",\"doi\":\"10.1016/j.radonc.2024.110593\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><div>The current deep learning-based dose prediction methods only predict one dose distribution. If the predicted dose is inaccurate, no additional options can be selected. To overcome this limitation, we propose a novel dose prediction method called “dose-band prediction,” which provides a spectrum of predicted dose distributions for planning and quality assurance (QA) purposes.</div></div><div><h3>Material and methods</h3><div>We utilized Upper/Lower-band losses in 3D neural networks to establish the Upper/Lower-band models (UBM/LBM). The maximum/minimum rational dose predicted in UBM/LBM defined the ideal dose spectrum for each voxel. We enrolled 104 nasopharyngeal carcinoma cases with tomotherapy (dataset 1), 54 cervical carcinoma cases with IMRT (dataset 2), and 37 cervical carcinoma cases with VMAT (dataset 3) in the study. Moreover, a dose band-based auto planning (Auto-plan<sub>dose-band</sub>) attempt was carried out in dataset 3, compared with the MSE model (Auto-plan<sub>MSE</sub>).</div></div><div><h3>Results</h3><div>The UBM/LBM doses tend to be higher/lower than the clinical dose, forming a predicted dose spectrum. The Middle-line dose represents the average of the Upper/Lower-band, which was consistent with the clinical dose. The mean differences of the planning target volumes (PTVs) and organs at risk (OARs) for the Upper-band, Middle-line, and Lower-band in Dataset 1 were 3.66 %, −0.40 %, and −4.48 % in Dataset 2, they were 2.40 %, −1.62 %, and −5.57 %; in Dataset 3, they were 2.18 %, −0.59 %, and −3.31 %. When PTVs meet prescription, the mean difference between Auto-plan<sub>dose-band</sub> and Auto-plan<sub>MSE</sub> in OARs was −2.67 %.</div></div><div><h3>Conclusion</h3><div>The dose-band prediction successfully predicted a spectrum of doses, making auto-planning and QA flexible and high quality.</div></div>\",\"PeriodicalId\":21041,\"journal\":{\"name\":\"Radiotherapy and Oncology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiotherapy and Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167814024035710\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiotherapy and Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167814024035710","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
A feasibility study of dose-band prediction in radiation therapy: Predicting a spectrum of plan dose
Purpose
The current deep learning-based dose prediction methods only predict one dose distribution. If the predicted dose is inaccurate, no additional options can be selected. To overcome this limitation, we propose a novel dose prediction method called “dose-band prediction,” which provides a spectrum of predicted dose distributions for planning and quality assurance (QA) purposes.
Material and methods
We utilized Upper/Lower-band losses in 3D neural networks to establish the Upper/Lower-band models (UBM/LBM). The maximum/minimum rational dose predicted in UBM/LBM defined the ideal dose spectrum for each voxel. We enrolled 104 nasopharyngeal carcinoma cases with tomotherapy (dataset 1), 54 cervical carcinoma cases with IMRT (dataset 2), and 37 cervical carcinoma cases with VMAT (dataset 3) in the study. Moreover, a dose band-based auto planning (Auto-plandose-band) attempt was carried out in dataset 3, compared with the MSE model (Auto-planMSE).
Results
The UBM/LBM doses tend to be higher/lower than the clinical dose, forming a predicted dose spectrum. The Middle-line dose represents the average of the Upper/Lower-band, which was consistent with the clinical dose. The mean differences of the planning target volumes (PTVs) and organs at risk (OARs) for the Upper-band, Middle-line, and Lower-band in Dataset 1 were 3.66 %, −0.40 %, and −4.48 % in Dataset 2, they were 2.40 %, −1.62 %, and −5.57 %; in Dataset 3, they were 2.18 %, −0.59 %, and −3.31 %. When PTVs meet prescription, the mean difference between Auto-plandose-band and Auto-planMSE in OARs was −2.67 %.
Conclusion
The dose-band prediction successfully predicted a spectrum of doses, making auto-planning and QA flexible and high quality.
期刊介绍:
Radiotherapy and Oncology publishes papers describing original research as well as review articles. It covers areas of interest relating to radiation oncology. This includes: clinical radiotherapy, combined modality treatment, translational studies, epidemiological outcomes, imaging, dosimetry, and radiation therapy planning, experimental work in radiobiology, chemobiology, hyperthermia and tumour biology, as well as data science in radiation oncology and physics aspects relevant to oncology.Papers on more general aspects of interest to the radiation oncologist including chemotherapy, surgery and immunology are also published.