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Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study

BACKGROUND: This study aimed to develop and validate a computed tomography (CT)-based radiomics model to predict microsatellite instability (MSI) status in colorectal cancer patients and to identify the radiomics signature with the most robust and high performance from one of the three phases of tri...

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Autores principales: Cao, Yuntai, Zhang, Guojin, Zhang, Jing, Yang, Yingjie, Ren, Jialiang, Yan, Xiaohong, Wang, Zhan, Zhao, Zhiyong, Huang, Xiaoyu, Bao, Haihua, Zhou, Junlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8222982/
https://www.ncbi.nlm.nih.gov/pubmed/34178682
http://dx.doi.org/10.3389/fonc.2021.687771
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author Cao, Yuntai
Zhang, Guojin
Zhang, Jing
Yang, Yingjie
Ren, Jialiang
Yan, Xiaohong
Wang, Zhan
Zhao, Zhiyong
Huang, Xiaoyu
Bao, Haihua
Zhou, Junlin
author_facet Cao, Yuntai
Zhang, Guojin
Zhang, Jing
Yang, Yingjie
Ren, Jialiang
Yan, Xiaohong
Wang, Zhan
Zhao, Zhiyong
Huang, Xiaoyu
Bao, Haihua
Zhou, Junlin
author_sort Cao, Yuntai
collection PubMed
description BACKGROUND: This study aimed to develop and validate a computed tomography (CT)-based radiomics model to predict microsatellite instability (MSI) status in colorectal cancer patients and to identify the radiomics signature with the most robust and high performance from one of the three phases of triphasic enhanced CT. METHODS: In total, 502 colorectal cancer patients with preoperative contrast-enhanced CT images and available MSI status (441 in the training cohort and 61 in the external validation cohort) were enrolled from two centers in our retrospective study. Radiomics features of the entire primary tumor were extracted from arterial-, delayed-, and venous-phase CT images. The least absolute shrinkage and selection operator method was used to retain the features closely associated with MSI status. Radiomics, clinical, and combined Clinical Radiomics models were built to predict MSI status. Model performance was evaluated by receiver operating characteristic curve analysis. RESULTS: Thirty-two radiomics features showed significant correlation with MSI status. Delayed-phase models showed superior predictive performance compared to arterial- or venous-phase models. Additionally, age, location, and carcinoembryonic antigen were considered useful predictors of MSI status. The Clinical Radiomics nomogram that incorporated both clinical risk factors and radiomics parameters showed excellent performance, with an AUC, accuracy, and sensitivity of 0.898, 0.837, and 0.821 in the training cohort and 0.964, 0.918, and 1.000 in the validation cohort, respectively. CONCLUSIONS: The proposed CT-based radiomics signature has excellent performance in predicting MSI status and could potentially guide individualized therapy.
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spelling pubmed-82229822021-06-25 Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study Cao, Yuntai Zhang, Guojin Zhang, Jing Yang, Yingjie Ren, Jialiang Yan, Xiaohong Wang, Zhan Zhao, Zhiyong Huang, Xiaoyu Bao, Haihua Zhou, Junlin Front Oncol Oncology BACKGROUND: This study aimed to develop and validate a computed tomography (CT)-based radiomics model to predict microsatellite instability (MSI) status in colorectal cancer patients and to identify the radiomics signature with the most robust and high performance from one of the three phases of triphasic enhanced CT. METHODS: In total, 502 colorectal cancer patients with preoperative contrast-enhanced CT images and available MSI status (441 in the training cohort and 61 in the external validation cohort) were enrolled from two centers in our retrospective study. Radiomics features of the entire primary tumor were extracted from arterial-, delayed-, and venous-phase CT images. The least absolute shrinkage and selection operator method was used to retain the features closely associated with MSI status. Radiomics, clinical, and combined Clinical Radiomics models were built to predict MSI status. Model performance was evaluated by receiver operating characteristic curve analysis. RESULTS: Thirty-two radiomics features showed significant correlation with MSI status. Delayed-phase models showed superior predictive performance compared to arterial- or venous-phase models. Additionally, age, location, and carcinoembryonic antigen were considered useful predictors of MSI status. The Clinical Radiomics nomogram that incorporated both clinical risk factors and radiomics parameters showed excellent performance, with an AUC, accuracy, and sensitivity of 0.898, 0.837, and 0.821 in the training cohort and 0.964, 0.918, and 1.000 in the validation cohort, respectively. CONCLUSIONS: The proposed CT-based radiomics signature has excellent performance in predicting MSI status and could potentially guide individualized therapy. Frontiers Media S.A. 2021-06-10 /pmc/articles/PMC8222982/ /pubmed/34178682 http://dx.doi.org/10.3389/fonc.2021.687771 Text en Copyright © 2021 Cao, Zhang, Zhang, Yang, Ren, Yan, Wang, Zhao, Huang, Bao and Zhou 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
Cao, Yuntai
Zhang, Guojin
Zhang, Jing
Yang, Yingjie
Ren, Jialiang
Yan, Xiaohong
Wang, Zhan
Zhao, Zhiyong
Huang, Xiaoyu
Bao, Haihua
Zhou, Junlin
Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study
title Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study
title_full Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study
title_fullStr Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study
title_full_unstemmed Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study
title_short Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study
title_sort predicting microsatellite instability status in colorectal cancer based on triphasic enhanced computed tomography radiomics signatures: a multicenter study
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8222982/
https://www.ncbi.nlm.nih.gov/pubmed/34178682
http://dx.doi.org/10.3389/fonc.2021.687771
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