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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)...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
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.
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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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