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MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer

BACKGROUND: To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC). METH...

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Autores principales: Song, Maxiaowei, Li, Shuai, Wang, Hongzhi, Hu, Ke, Wang, Fengwei, Teng, Huajing, Wang, Zhi, Liu, Jin, Jia, Angela Y., Cai, Yong, Li, Yongheng, Zhu, Xianggao, Geng, Jianhao, Zhang, Yangzi, Wan, XiangBo, Wang, Weihu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9296479/
https://www.ncbi.nlm.nih.gov/pubmed/35368044
http://dx.doi.org/10.1038/s41416-022-01786-7
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author Song, Maxiaowei
Li, Shuai
Wang, Hongzhi
Hu, Ke
Wang, Fengwei
Teng, Huajing
Wang, Zhi
Liu, Jin
Jia, Angela Y.
Cai, Yong
Li, Yongheng
Zhu, Xianggao
Geng, Jianhao
Zhang, Yangzi
Wan, XiangBo
Wang, Weihu
author_facet Song, Maxiaowei
Li, Shuai
Wang, Hongzhi
Hu, Ke
Wang, Fengwei
Teng, Huajing
Wang, Zhi
Liu, Jin
Jia, Angela Y.
Cai, Yong
Li, Yongheng
Zhu, Xianggao
Geng, Jianhao
Zhang, Yangzi
Wan, XiangBo
Wang, Weihu
author_sort Song, Maxiaowei
collection PubMed
description BACKGROUND: To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC). METHODS: Baseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models’ development and validation. RESULTS: We finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance. CONCLUSIONS: The performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC’s true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies.
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spelling pubmed-92964792022-07-21 MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer Song, Maxiaowei Li, Shuai Wang, Hongzhi Hu, Ke Wang, Fengwei Teng, Huajing Wang, Zhi Liu, Jin Jia, Angela Y. Cai, Yong Li, Yongheng Zhu, Xianggao Geng, Jianhao Zhang, Yangzi Wan, XiangBo Wang, Weihu Br J Cancer Article BACKGROUND: To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC). METHODS: Baseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models’ development and validation. RESULTS: We finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance. CONCLUSIONS: The performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC’s true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies. Nature Publishing Group UK 2022-04-02 2022-07-20 /pmc/articles/PMC9296479/ /pubmed/35368044 http://dx.doi.org/10.1038/s41416-022-01786-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Song, Maxiaowei
Li, Shuai
Wang, Hongzhi
Hu, Ke
Wang, Fengwei
Teng, Huajing
Wang, Zhi
Liu, Jin
Jia, Angela Y.
Cai, Yong
Li, Yongheng
Zhu, Xianggao
Geng, Jianhao
Zhang, Yangzi
Wan, XiangBo
Wang, Weihu
MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer
title MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer
title_full MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer
title_fullStr MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer
title_full_unstemmed MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer
title_short MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer
title_sort mri radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9296479/
https://www.ncbi.nlm.nih.gov/pubmed/35368044
http://dx.doi.org/10.1038/s41416-022-01786-7
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