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Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy
PURPOSE: The purpose of this study was to evaluate the accuracy of a lung stereotactic body radiotherapy (SBRT) treatment plan with the target of a newly predicted internal target volume (ITV(predict)) and the feasibility of its clinical application. ITV(predict) was automatically generated by our i...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8980420/ https://www.ncbi.nlm.nih.gov/pubmed/35392465 http://dx.doi.org/10.3389/fpubh.2022.860135 |
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author | Zhang, Shujun Lv, Bo Zheng, Xiangpeng Li, Ya Ge, Weiqiang Zhang, Libo Mo, Fan Qiu, Jianjian |
author_facet | Zhang, Shujun Lv, Bo Zheng, Xiangpeng Li, Ya Ge, Weiqiang Zhang, Libo Mo, Fan Qiu, Jianjian |
author_sort | Zhang, Shujun |
collection | PubMed |
description | PURPOSE: The purpose of this study was to evaluate the accuracy of a lung stereotactic body radiotherapy (SBRT) treatment plan with the target of a newly predicted internal target volume (ITV(predict)) and the feasibility of its clinical application. ITV(predict) was automatically generated by our in-house deep learning model according to the cone-beam CT (CBCT) image database. METHOD: A retrospective study of 45 patients who underwent SBRT was involved, and Mask R-CNN based algorithm model helped to predict the internal target volume (ITV) using the CBCT image database. The geometric accuracy of ITV(predict) was verified by the Dice Similarity Coefficient (DSC), 3D Motion Range (R(3D)), Relative Volume Index (RVI), and Hausdorff Distance (HD). The PTV(predict) was generated by ITV(predict), which was registered and then projected on free-breath CT (FBCT) images. The PTV(FBCT) was margined from the GTV on FBCT images gross tumor volume on free-breath CT (GTV(FBCT)). Treatment plans with the target of Predict planning target volume on CBCT images (PTV(predict)) and planning target volume on free-breath CT (PTV(FBCT)) were respectively re-established, and the dosimetric parameters included the ratio of the volume of patients receiving at least the prescribed dose to the volume of PTV (R(100%)), the ratio of the volume of patients receiving at least 50% of the prescribed dose to the volume of PTV in the Radiation Therapy Oncology Group (RTOG) 0813 Trial (R(50%)), Gradient Index (GI), and the maximum dose 2 cm from the PTV (D(2cm)), which were evaluated via Plan(4DCT), plan which based on PTV(predict) (Plan(predict)), and plan which based on PTV(FBCT) (Plan(FBCT)). RESULT: The geometric results showed that there existed a good correlation between ITV(predict) and ITV on the 4-dimensional CT [ITV(4DCT); DSC= 0.83 ±0.18]. However, the average volume of ITV(predict) was 10% less than that of ITV(4DCT) (p = 0.333). No significant difference in dose coverage was found in V(100%) for the ITV with 99.98 ± 0.04% in the ITV(4DCT) vs. 97.56 ± 4.71% in the ITV(predict) (p = 0.162). Dosimetry parameters of PTV, including R(100%), R(50%), GI and D(2cm) showed no statistically significant difference between each plan (p > 0.05). CONCLUSION: Dosimetric parameters of Plan(predict) are clinically comparable to those of the original Plan(4DCT.) This study confirmed that the treatment plan based on ITV(predict) produced by our model could automatically meet clinical requirements. Thus, for patients undergoing lung SBRT, the model has great potential for using CBCT images for ITV contouring which can be used in treatment planning. |
format | Online Article Text |
id | pubmed-8980420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89804202022-04-06 Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy Zhang, Shujun Lv, Bo Zheng, Xiangpeng Li, Ya Ge, Weiqiang Zhang, Libo Mo, Fan Qiu, Jianjian Front Public Health Public Health PURPOSE: The purpose of this study was to evaluate the accuracy of a lung stereotactic body radiotherapy (SBRT) treatment plan with the target of a newly predicted internal target volume (ITV(predict)) and the feasibility of its clinical application. ITV(predict) was automatically generated by our in-house deep learning model according to the cone-beam CT (CBCT) image database. METHOD: A retrospective study of 45 patients who underwent SBRT was involved, and Mask R-CNN based algorithm model helped to predict the internal target volume (ITV) using the CBCT image database. The geometric accuracy of ITV(predict) was verified by the Dice Similarity Coefficient (DSC), 3D Motion Range (R(3D)), Relative Volume Index (RVI), and Hausdorff Distance (HD). The PTV(predict) was generated by ITV(predict), which was registered and then projected on free-breath CT (FBCT) images. The PTV(FBCT) was margined from the GTV on FBCT images gross tumor volume on free-breath CT (GTV(FBCT)). Treatment plans with the target of Predict planning target volume on CBCT images (PTV(predict)) and planning target volume on free-breath CT (PTV(FBCT)) were respectively re-established, and the dosimetric parameters included the ratio of the volume of patients receiving at least the prescribed dose to the volume of PTV (R(100%)), the ratio of the volume of patients receiving at least 50% of the prescribed dose to the volume of PTV in the Radiation Therapy Oncology Group (RTOG) 0813 Trial (R(50%)), Gradient Index (GI), and the maximum dose 2 cm from the PTV (D(2cm)), which were evaluated via Plan(4DCT), plan which based on PTV(predict) (Plan(predict)), and plan which based on PTV(FBCT) (Plan(FBCT)). RESULT: The geometric results showed that there existed a good correlation between ITV(predict) and ITV on the 4-dimensional CT [ITV(4DCT); DSC= 0.83 ±0.18]. However, the average volume of ITV(predict) was 10% less than that of ITV(4DCT) (p = 0.333). No significant difference in dose coverage was found in V(100%) for the ITV with 99.98 ± 0.04% in the ITV(4DCT) vs. 97.56 ± 4.71% in the ITV(predict) (p = 0.162). Dosimetry parameters of PTV, including R(100%), R(50%), GI and D(2cm) showed no statistically significant difference between each plan (p > 0.05). CONCLUSION: Dosimetric parameters of Plan(predict) are clinically comparable to those of the original Plan(4DCT.) This study confirmed that the treatment plan based on ITV(predict) produced by our model could automatically meet clinical requirements. Thus, for patients undergoing lung SBRT, the model has great potential for using CBCT images for ITV contouring which can be used in treatment planning. Frontiers Media S.A. 2022-03-22 /pmc/articles/PMC8980420/ /pubmed/35392465 http://dx.doi.org/10.3389/fpubh.2022.860135 Text en Copyright © 2022 Zhang, Lv, Zheng, Li, Ge, Zhang, Mo and Qiu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Zhang, Shujun Lv, Bo Zheng, Xiangpeng Li, Ya Ge, Weiqiang Zhang, Libo Mo, Fan Qiu, Jianjian Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy |
title | Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy |
title_full | Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy |
title_fullStr | Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy |
title_full_unstemmed | Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy |
title_short | Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy |
title_sort | dosimetric study of deep learning-guided itv prediction in cone-beam ct for lung stereotactic body radiotherapy |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8980420/ https://www.ncbi.nlm.nih.gov/pubmed/35392465 http://dx.doi.org/10.3389/fpubh.2022.860135 |
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