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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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. |
format | Online Article Text |
id | pubmed-8222982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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