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Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer
In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing progn...
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/PMC9669703/ https://www.ncbi.nlm.nih.gov/pubmed/36408187 http://dx.doi.org/10.3389/fonc.2022.901287 |
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author | Xie, Fei Zhao, Qin Li, Shuqi Wu, Shuangshuang Li, Jinli Li, Haojiang Chen, Shenghuan Jiang, Wu Dong, Annan Wu, Liqing Liu, Long Huang, Huabin Xu, Shuoyu Shao, Yuanzhi Liu, Lizhi Li, Li Cai, Peiqiang |
author_facet | Xie, Fei Zhao, Qin Li, Shuqi Wu, Shuangshuang Li, Jinli Li, Haojiang Chen, Shenghuan Jiang, Wu Dong, Annan Wu, Liqing Liu, Long Huang, Huabin Xu, Shuoyu Shao, Yuanzhi Liu, Lizhi Li, Li Cai, Peiqiang |
author_sort | Xie, Fei |
collection | PubMed |
description | In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC. |
format | Online Article Text |
id | pubmed-9669703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96697032022-11-18 Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer Xie, Fei Zhao, Qin Li, Shuqi Wu, Shuangshuang Li, Jinli Li, Haojiang Chen, Shenghuan Jiang, Wu Dong, Annan Wu, Liqing Liu, Long Huang, Huabin Xu, Shuoyu Shao, Yuanzhi Liu, Lizhi Li, Li Cai, Peiqiang Front Oncol Oncology In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC. Frontiers Media S.A. 2022-11-03 /pmc/articles/PMC9669703/ /pubmed/36408187 http://dx.doi.org/10.3389/fonc.2022.901287 Text en Copyright © 2022 Xie, Zhao, Li, Wu, Li, Li, Chen, Jiang, Dong, Wu, Liu, Huang, Xu, Shao, Liu, Li and Cai 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 | Oncology Xie, Fei Zhao, Qin Li, Shuqi Wu, Shuangshuang Li, Jinli Li, Haojiang Chen, Shenghuan Jiang, Wu Dong, Annan Wu, Liqing Liu, Long Huang, Huabin Xu, Shuoyu Shao, Yuanzhi Liu, Lizhi Li, Li Cai, Peiqiang Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer |
title | Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer |
title_full | Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer |
title_fullStr | Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer |
title_full_unstemmed | Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer |
title_short | Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer |
title_sort | establishment and validation of novel mri radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669703/ https://www.ncbi.nlm.nih.gov/pubmed/36408187 http://dx.doi.org/10.3389/fonc.2022.901287 |
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