A predictive model for pain response following radiotherapy for treatment of spinal metastases

Abstract To establish a predictive model for pain response following radiotherapy using a combination of radiomic and clinical features of spinal metastasis. This retrospective study enrolled patients with painful spine metastases who received palliative radiation therapy from 2018 to 2019. Pain res...

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Autores principales:	Kohei Wakabayashi, Yutaro Koide, Takahiro Aoyama, Hidetoshi Shimizu, Risei Miyauchi, Hiroshi Tanaka, Hiroyuki Tachibana, Katsumasa Nakamura, Takeshi Kodaira
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/11afa5c093ef4e91b4f2052931e47a43
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spelling	oai:doaj.org-article:11afa5c093ef4e91b4f2052931e47a432021-12-02T17:23:26ZA predictive model for pain response following radiotherapy for treatment of spinal metastases10.1038/s41598-021-92363-02045-2322https://doaj.org/article/11afa5c093ef4e91b4f2052931e47a432021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92363-0https://doaj.org/toc/2045-2322Abstract To establish a predictive model for pain response following radiotherapy using a combination of radiomic and clinical features of spinal metastasis. This retrospective study enrolled patients with painful spine metastases who received palliative radiation therapy from 2018 to 2019. Pain response was defined using the International Consensus Criteria. The clinical and radiomic features were extracted from medical records and pre-treatment CT images. Feature selection was performed and a random forests ensemble learning method was used to build a predictive model. Area under the curve (AUC) was used as a predictive performance metric. 69 patients were enrolled with 48 patients showing a response. Random forest models built on the radiomic, clinical, and ‘combined’ features achieved an AUC of 0.824, 0.702, 0.848, respectively. The sensitivity and specificity of the combined features model were 85.4% and 76.2%, at the best diagnostic decision point. We built a pain response model in patients with spinal metastases using a combination of clinical and radiomic features. To the best of our knowledge, we are the first to examine pain response using pre-treatment CT radiomic features. Our model showed the potential to predict patients who respond to radiation therapy.Kohei WakabayashiYutaro KoideTakahiro AoyamaHidetoshi ShimizuRisei MiyauchiHiroshi TanakaHiroyuki TachibanaKatsumasa NakamuraTakeshi KodairaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kohei Wakabayashi Yutaro Koide Takahiro Aoyama Hidetoshi Shimizu Risei Miyauchi Hiroshi Tanaka Hiroyuki Tachibana Katsumasa Nakamura Takeshi Kodaira A predictive model for pain response following radiotherapy for treatment of spinal metastases
description	Abstract To establish a predictive model for pain response following radiotherapy using a combination of radiomic and clinical features of spinal metastasis. This retrospective study enrolled patients with painful spine metastases who received palliative radiation therapy from 2018 to 2019. Pain response was defined using the International Consensus Criteria. The clinical and radiomic features were extracted from medical records and pre-treatment CT images. Feature selection was performed and a random forests ensemble learning method was used to build a predictive model. Area under the curve (AUC) was used as a predictive performance metric. 69 patients were enrolled with 48 patients showing a response. Random forest models built on the radiomic, clinical, and ‘combined’ features achieved an AUC of 0.824, 0.702, 0.848, respectively. The sensitivity and specificity of the combined features model were 85.4% and 76.2%, at the best diagnostic decision point. We built a pain response model in patients with spinal metastases using a combination of clinical and radiomic features. To the best of our knowledge, we are the first to examine pain response using pre-treatment CT radiomic features. Our model showed the potential to predict patients who respond to radiation therapy.
format	article
author	Kohei Wakabayashi Yutaro Koide Takahiro Aoyama Hidetoshi Shimizu Risei Miyauchi Hiroshi Tanaka Hiroyuki Tachibana Katsumasa Nakamura Takeshi Kodaira
author_facet	Kohei Wakabayashi Yutaro Koide Takahiro Aoyama Hidetoshi Shimizu Risei Miyauchi Hiroshi Tanaka Hiroyuki Tachibana Katsumasa Nakamura Takeshi Kodaira
author_sort	Kohei Wakabayashi
title	A predictive model for pain response following radiotherapy for treatment of spinal metastases
title_short	A predictive model for pain response following radiotherapy for treatment of spinal metastases
title_full	A predictive model for pain response following radiotherapy for treatment of spinal metastases
title_fullStr	A predictive model for pain response following radiotherapy for treatment of spinal metastases
title_full_unstemmed	A predictive model for pain response following radiotherapy for treatment of spinal metastases
title_sort	predictive model for pain response following radiotherapy for treatment of spinal metastases
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/11afa5c093ef4e91b4f2052931e47a43
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A predictive model for pain response following radiotherapy for treatment of spinal metastases

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