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Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy
To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma. And simulated radiotherapy plans with same dose-volume-histogram (DVH)...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690639/ https://www.ncbi.nlm.nih.gov/pubmed/38047064 http://dx.doi.org/10.1016/j.isci.2023.108394 |
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author | Yang, Shan-Shan Peng, Qing-He Wu, Ai-Qian Zhang, Bao-Yu Liu, Zhi-Qiao Chen, En-Ni Xie, Fang-Yun OuYang, Pu-Yun Chen, Chun-Yan |
author_facet | Yang, Shan-Shan Peng, Qing-He Wu, Ai-Qian Zhang, Bao-Yu Liu, Zhi-Qiao Chen, En-Ni Xie, Fang-Yun OuYang, Pu-Yun Chen, Chun-Yan |
author_sort | Yang, Shan-Shan |
collection | PubMed |
description | To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma. And simulated radiotherapy plans with same dose-volume-histogram (DVH) but different dose distributions were redesigned to explore the clinical application of the multiview radiomics risk model. The radiomics and dosiomics were built based on selected radiomics and dosiomics features from planning computed tomography and dose distribution, respectively. The multiview radiomics risk model that integrated radiomics, dosiomics, DVH parameters, and clinical factors had better performance than traditional normal tissue complication probability models. And multiview radiomics risk model could identify differences of patient hypothyroidism-free survival that cannot be stratified by traditional models. Besides, two redesigned simulated plans further verified the clinical application and advantage of the multiview radiomics risk model. The multiview radiomics risk model was a promising method to predict radiation-induced hypothyroidism and guide individualized IMRT. |
format | Online Article Text |
id | pubmed-10690639 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-106906392023-12-02 Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy Yang, Shan-Shan Peng, Qing-He Wu, Ai-Qian Zhang, Bao-Yu Liu, Zhi-Qiao Chen, En-Ni Xie, Fang-Yun OuYang, Pu-Yun Chen, Chun-Yan iScience Article To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma. And simulated radiotherapy plans with same dose-volume-histogram (DVH) but different dose distributions were redesigned to explore the clinical application of the multiview radiomics risk model. The radiomics and dosiomics were built based on selected radiomics and dosiomics features from planning computed tomography and dose distribution, respectively. The multiview radiomics risk model that integrated radiomics, dosiomics, DVH parameters, and clinical factors had better performance than traditional normal tissue complication probability models. And multiview radiomics risk model could identify differences of patient hypothyroidism-free survival that cannot be stratified by traditional models. Besides, two redesigned simulated plans further verified the clinical application and advantage of the multiview radiomics risk model. The multiview radiomics risk model was a promising method to predict radiation-induced hypothyroidism and guide individualized IMRT. Elsevier 2023-11-04 /pmc/articles/PMC10690639/ /pubmed/38047064 http://dx.doi.org/10.1016/j.isci.2023.108394 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Yang, Shan-Shan Peng, Qing-He Wu, Ai-Qian Zhang, Bao-Yu Liu, Zhi-Qiao Chen, En-Ni Xie, Fang-Yun OuYang, Pu-Yun Chen, Chun-Yan Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy |
title | Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy |
title_full | Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy |
title_fullStr | Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy |
title_full_unstemmed | Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy |
title_short | Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy |
title_sort | radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690639/ https://www.ncbi.nlm.nih.gov/pubmed/38047064 http://dx.doi.org/10.1016/j.isci.2023.108394 |
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