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Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer

OBJECTIVE: To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural sp...

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Autores principales: Dong, Xue, Ren, Gang, Chen, Yanhong, Yong, Huifang, Zhang, Tingting, Yin, Qiufeng, Zhang, Zhongyang, Yuan, Shijun, Ge, Yaqiong, Duan, Shaofeng, Liu, Huanhuan, Wang, Dengbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614283/
https://www.ncbi.nlm.nih.gov/pubmed/37909021
http://dx.doi.org/10.3389/fonc.2023.1194120
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author Dong, Xue
Ren, Gang
Chen, Yanhong
Yong, Huifang
Zhang, Tingting
Yin, Qiufeng
Zhang, Zhongyang
Yuan, Shijun
Ge, Yaqiong
Duan, Shaofeng
Liu, Huanhuan
Wang, Dengbin
author_facet Dong, Xue
Ren, Gang
Chen, Yanhong
Yong, Huifang
Zhang, Tingting
Yin, Qiufeng
Zhang, Zhongyang
Yuan, Shijun
Ge, Yaqiong
Duan, Shaofeng
Liu, Huanhuan
Wang, Dengbin
author_sort Dong, Xue
collection PubMed
description OBJECTIVE: To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5 mm). METHODS: This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors). RESULTS: Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416. A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful. CONCLUSION: T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies.
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spelling pubmed-106142832023-10-31 Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer Dong, Xue Ren, Gang Chen, Yanhong Yong, Huifang Zhang, Tingting Yin, Qiufeng Zhang, Zhongyang Yuan, Shijun Ge, Yaqiong Duan, Shaofeng Liu, Huanhuan Wang, Dengbin Front Oncol Oncology OBJECTIVE: To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5 mm). METHODS: This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors). RESULTS: Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416. A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful. CONCLUSION: T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies. Frontiers Media S.A. 2023-10-16 /pmc/articles/PMC10614283/ /pubmed/37909021 http://dx.doi.org/10.3389/fonc.2023.1194120 Text en Copyright © 2023 Dong, Ren, Chen, Yong, Zhang, Yin, Zhang, Yuan, Ge, Duan, Liu 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
Dong, Xue
Ren, Gang
Chen, Yanhong
Yong, Huifang
Zhang, Tingting
Yin, Qiufeng
Zhang, Zhongyang
Yuan, Shijun
Ge, Yaqiong
Duan, Shaofeng
Liu, Huanhuan
Wang, Dengbin
Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer
title Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer
title_full Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer
title_fullStr Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer
title_full_unstemmed Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer
title_short Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer
title_sort effects of mri radiomics combined with clinical data in evaluating lymph node metastasis in mrt1-3a staging rectal cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614283/
https://www.ncbi.nlm.nih.gov/pubmed/37909021
http://dx.doi.org/10.3389/fonc.2023.1194120
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