{"title":"A feasibility study of tumor motion monitoring for SBRT of lung cancer based on 3D point cloud detection and stacking ensemble learning","authors":"","doi":"10.1016/j.jmir.2024.101729","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>To construct a tumor motion monitoring model for stereotactic body radiation therapy (SBRT) of lung cancer from a feasibility perspective.</p></div><div><h3>Methods</h3><p>A total of 32 treatment plans for 22 patients were collected, whose planning CT and the centroid position of the planning target volume (PTV) were used as the reference. Images of different respiratory phases in 4DCT were acquired to redefine the targets and obtain the floating PTV centroid positions. In accordance with the planning CT and CBCT registration parameters, data augmentation was accomplished, yielding 2130 experimental recordings for analysis. We employed a stacking multi-learning ensemble approach to fit the 3D point cloud variations of body surface and the change of target position to construct the tumor motion monitoring model, and the prediction accuracy was assess using root mean squared error (RMSE) and R-Square (R<sup>2</sup>).</p></div><div><h3>Results</h3><p>The prediction displacement of the stacking ensemble model shows a high degree of agreement with the reference value in each direction. In the first layer of model, the <em>X</em> direction (<em>RMSE</em> =0.019 ∼ 0.145<em>mm, R<sup>2</sup></em> =0.9793∼0.9996) and the <em>Z</em> direction (<em>RMSE</em> = 0.051 ∼ 0.168 <em>mm, R<sup>2</sup></em> = 0.9736∼0.9976) show the best results, while the <em>Y</em> direction ranked behind (<em>RMSE</em> = 0.088 ∼ 0.224 <em>mm, R<sup>2</sup></em> = 0.9553∼ 0.9933). The second layer model summarizes the advantages of unit models of first layer, and <em>RMSE</em> of 0.015 <em>mm</em>, 0.083 <em>mm</em>, 0.041 <em>mm</em>, and <em>R<sup>2</sup></em> of 0.9998, 0.9931, 0.9984 respectively for <em>X, Y, Z</em> were obtained.</p></div><div><h3>Conclusions</h3><p>The tumor motion monitoring method for SBRT of lung cancer has potential application of non-ionization, non-invasive, markerless, and real-time.</p></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Imaging and Radiation Sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1939865424004600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
To construct a tumor motion monitoring model for stereotactic body radiation therapy (SBRT) of lung cancer from a feasibility perspective.
Methods
A total of 32 treatment plans for 22 patients were collected, whose planning CT and the centroid position of the planning target volume (PTV) were used as the reference. Images of different respiratory phases in 4DCT were acquired to redefine the targets and obtain the floating PTV centroid positions. In accordance with the planning CT and CBCT registration parameters, data augmentation was accomplished, yielding 2130 experimental recordings for analysis. We employed a stacking multi-learning ensemble approach to fit the 3D point cloud variations of body surface and the change of target position to construct the tumor motion monitoring model, and the prediction accuracy was assess using root mean squared error (RMSE) and R-Square (R2).
Results
The prediction displacement of the stacking ensemble model shows a high degree of agreement with the reference value in each direction. In the first layer of model, the X direction (RMSE =0.019 ∼ 0.145mm, R2 =0.9793∼0.9996) and the Z direction (RMSE = 0.051 ∼ 0.168 mm, R2 = 0.9736∼0.9976) show the best results, while the Y direction ranked behind (RMSE = 0.088 ∼ 0.224 mm, R2 = 0.9553∼ 0.9933). The second layer model summarizes the advantages of unit models of first layer, and RMSE of 0.015 mm, 0.083 mm, 0.041 mm, and R2 of 0.9998, 0.9931, 0.9984 respectively for X, Y, Z were obtained.
Conclusions
The tumor motion monitoring method for SBRT of lung cancer has potential application of non-ionization, non-invasive, markerless, and real-time.
期刊介绍:
Journal of Medical Imaging and Radiation Sciences is the official peer-reviewed journal of the Canadian Association of Medical Radiation Technologists. This journal is published four times a year and is circulated to approximately 11,000 medical radiation technologists, libraries and radiology departments throughout Canada, the United States and overseas. The Journal publishes articles on recent research, new technology and techniques, professional practices, technologists viewpoints as well as relevant book reviews.