Cargando…
Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer
BACKGROUND: Tumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based r...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989162/ https://www.ncbi.nlm.nih.gov/pubmed/36895487 http://dx.doi.org/10.3389/fonc.2023.1081134 |
_version_ | 1784901707310825472 |
---|---|
author | Yue, Xiaoning He, Xiaoyu He, Shuaijie Wu, Jingjing Fan, Wei Zhang, Haijun Wang, Chengwei |
author_facet | Yue, Xiaoning He, Xiaoyu He, Shuaijie Wu, Jingjing Fan, Wei Zhang, Haijun Wang, Chengwei |
author_sort | Yue, Xiaoning |
collection | PubMed |
description | BACKGROUND: Tumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC. METHODS: One hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI). RESULTS: In total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754–0.920), 0.875 (95% CI: 0.797–0.952), and 0.923 (95% CI: 0.869–0.977) for the training set; 0.857 (95% CI: 0.741–0.973), 0.785 (95% CI: 0.592–0.979), and 0.914 (95% CI: 0.827–0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214–1.061) and 0.657 (0.079–1.394), and IDIs were 0.115 (0.077–0.306) and 0.053 (0.027–0.357) in the training set and validation set, respectively. CONCLUSION: The radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage. |
format | Online Article Text |
id | pubmed-9989162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99891622023-03-08 Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer Yue, Xiaoning He, Xiaoyu He, Shuaijie Wu, Jingjing Fan, Wei Zhang, Haijun Wang, Chengwei Front Oncol Oncology BACKGROUND: Tumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC. METHODS: One hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI). RESULTS: In total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754–0.920), 0.875 (95% CI: 0.797–0.952), and 0.923 (95% CI: 0.869–0.977) for the training set; 0.857 (95% CI: 0.741–0.973), 0.785 (95% CI: 0.592–0.979), and 0.914 (95% CI: 0.827–0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214–1.061) and 0.657 (0.079–1.394), and IDIs were 0.115 (0.077–0.306) and 0.053 (0.027–0.357) in the training set and validation set, respectively. CONCLUSION: The radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage. Frontiers Media S.A. 2023-02-21 /pmc/articles/PMC9989162/ /pubmed/36895487 http://dx.doi.org/10.3389/fonc.2023.1081134 Text en Copyright © 2023 Yue, He, He, Wu, Fan, Zhang and Wang 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 Yue, Xiaoning He, Xiaoyu He, Shuaijie Wu, Jingjing Fan, Wei Zhang, Haijun Wang, Chengwei Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer |
title | Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer |
title_full | Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer |
title_fullStr | Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer |
title_full_unstemmed | Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer |
title_short | Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer |
title_sort | multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989162/ https://www.ncbi.nlm.nih.gov/pubmed/36895487 http://dx.doi.org/10.3389/fonc.2023.1081134 |
work_keys_str_mv | AT yuexiaoning multiparametricmagneticresonanceimagingbasedradiomicsnomogramforpredictingtumorgradeinendometrialcancer AT hexiaoyu multiparametricmagneticresonanceimagingbasedradiomicsnomogramforpredictingtumorgradeinendometrialcancer AT heshuaijie multiparametricmagneticresonanceimagingbasedradiomicsnomogramforpredictingtumorgradeinendometrialcancer AT wujingjing multiparametricmagneticresonanceimagingbasedradiomicsnomogramforpredictingtumorgradeinendometrialcancer AT fanwei multiparametricmagneticresonanceimagingbasedradiomicsnomogramforpredictingtumorgradeinendometrialcancer AT zhanghaijun multiparametricmagneticresonanceimagingbasedradiomicsnomogramforpredictingtumorgradeinendometrialcancer AT wangchengwei multiparametricmagneticresonanceimagingbasedradiomicsnomogramforpredictingtumorgradeinendometrialcancer |