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Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram

OBJECTIVE: To build and validate an MRI-based radiomics nomogram to predict the therapeutic response to neoadjuvant chemoradiotherapy (nCRT) in rectal mucinous adenocarcinoma (RMAC). METHODS: Totally, 92 individuals with pathologically confirmed RMAC administered surgical resection upon nCRT in two...

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Autores principales: Li, Zhihui, Li, Shuai, Zang, Shuqin, Ma, Xiaolu, Chen, Fangying, Xia, Yuwei, Chen, Liuping, Shen, Fu, Lu, Yong, Lu, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8181148/
https://www.ncbi.nlm.nih.gov/pubmed/34109121
http://dx.doi.org/10.3389/fonc.2021.671636
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author Li, Zhihui
Li, Shuai
Zang, Shuqin
Ma, Xiaolu
Chen, Fangying
Xia, Yuwei
Chen, Liuping
Shen, Fu
Lu, Yong
Lu, Jianping
author_facet Li, Zhihui
Li, Shuai
Zang, Shuqin
Ma, Xiaolu
Chen, Fangying
Xia, Yuwei
Chen, Liuping
Shen, Fu
Lu, Yong
Lu, Jianping
author_sort Li, Zhihui
collection PubMed
description OBJECTIVE: To build and validate an MRI-based radiomics nomogram to predict the therapeutic response to neoadjuvant chemoradiotherapy (nCRT) in rectal mucinous adenocarcinoma (RMAC). METHODS: Totally, 92 individuals with pathologically confirmed RMAC administered surgical resection upon nCRT in two different centers were assessed retrospectively (training set, n = 52, validation set, n = 40). Rectal MRI was performed pre-nCRT. Radiomics parameters were obtained from high-resolution T2-weighted images and selected to construct a radiomics signature. Then, radiomics nomogram construction integrated patient variables and the radiomics signature. The resulting radiomics nomogram was utilized to assess the tumor regression grade (TRG). Diagnostic performance was determined by generating receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS: Six optimal features related to TRG were obtained to construct a radiomics signature. The nomogram combining the radiomics signature with age and mucin deposit outperformed the radiomics signature alone in the training (AUC, 0.950 vs 0.843, p < 0.05) and validation (AUC, 0.868 vs 0.719, p < 0.05) cohorts. DCA demonstrated a clinical utility for the radiomics nomogram model. CONCLUSIONS: The established quantitative MRI-based radiomics nomogram is effective in predicting treatment response to neoadjuvant therapy in patients with RMAC.
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spelling pubmed-81811482021-06-08 Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram Li, Zhihui Li, Shuai Zang, Shuqin Ma, Xiaolu Chen, Fangying Xia, Yuwei Chen, Liuping Shen, Fu Lu, Yong Lu, Jianping Front Oncol Oncology OBJECTIVE: To build and validate an MRI-based radiomics nomogram to predict the therapeutic response to neoadjuvant chemoradiotherapy (nCRT) in rectal mucinous adenocarcinoma (RMAC). METHODS: Totally, 92 individuals with pathologically confirmed RMAC administered surgical resection upon nCRT in two different centers were assessed retrospectively (training set, n = 52, validation set, n = 40). Rectal MRI was performed pre-nCRT. Radiomics parameters were obtained from high-resolution T2-weighted images and selected to construct a radiomics signature. Then, radiomics nomogram construction integrated patient variables and the radiomics signature. The resulting radiomics nomogram was utilized to assess the tumor regression grade (TRG). Diagnostic performance was determined by generating receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS: Six optimal features related to TRG were obtained to construct a radiomics signature. The nomogram combining the radiomics signature with age and mucin deposit outperformed the radiomics signature alone in the training (AUC, 0.950 vs 0.843, p < 0.05) and validation (AUC, 0.868 vs 0.719, p < 0.05) cohorts. DCA demonstrated a clinical utility for the radiomics nomogram model. CONCLUSIONS: The established quantitative MRI-based radiomics nomogram is effective in predicting treatment response to neoadjuvant therapy in patients with RMAC. Frontiers Media S.A. 2021-05-24 /pmc/articles/PMC8181148/ /pubmed/34109121 http://dx.doi.org/10.3389/fonc.2021.671636 Text en Copyright © 2021 Li, Li, Zang, Ma, Chen, Xia, Chen, Shen, Lu and Lu 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
Li, Zhihui
Li, Shuai
Zang, Shuqin
Ma, Xiaolu
Chen, Fangying
Xia, Yuwei
Chen, Liuping
Shen, Fu
Lu, Yong
Lu, Jianping
Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram
title Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram
title_full Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram
title_fullStr Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram
title_full_unstemmed Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram
title_short Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram
title_sort predicting treatment response to neoadjuvant chemoradiotherapy in rectal mucinous adenocarcinoma using an mri-based radiomics nomogram
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8181148/
https://www.ncbi.nlm.nih.gov/pubmed/34109121
http://dx.doi.org/10.3389/fonc.2021.671636
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