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Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images
BACKGROUND: In colorectal cancer (CRC), mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype, morphology, and prognosis. However, mucinous components are present in a large number of adenocarcinomas, and the prognostic value of mucus proportion has not been investigated. Arti...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982603/ https://www.ncbi.nlm.nih.gov/pubmed/35693123 http://dx.doi.org/10.1093/pcmedi/pbab002 |
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author | Zhao, Ke Wu, Lin Huang, Yanqi Yao, Su Xu, Zeyan Lin, Huan Wang, Huihui Liang, Yanting Xu, Yao Chen, Xin Zhao, Minning Peng, Jiaming Huang, Yuli Liang, Changhong Li, Zhenhui Li, Yong Liu, Zaiyi |
author_facet | Zhao, Ke Wu, Lin Huang, Yanqi Yao, Su Xu, Zeyan Lin, Huan Wang, Huihui Liang, Yanting Xu, Yao Chen, Xin Zhao, Minning Peng, Jiaming Huang, Yuli Liang, Changhong Li, Zhenhui Li, Yong Liu, Zaiyi |
author_sort | Zhao, Ke |
collection | PubMed |
description | BACKGROUND: In colorectal cancer (CRC), mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype, morphology, and prognosis. However, mucinous components are present in a large number of adenocarcinomas, and the prognostic value of mucus proportion has not been investigated. Artificial intelligence provides a way to quantify mucus proportion on whole-slide images (WSIs) accurately. We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts. METHODS: Deep learning was used to segment WSIs stained with hematoxylin and eosin. Mucus-tumor ratio (MTR) was defined as the proportion of mucinous component in the tumor area. A training cohort (N = 419) and a validation cohort (N = 315) were used to evaluate the prognostic value of MTR. Survival analysis was performed using the Cox proportional hazard model. RESULT: Patients were stratified to mucus-low and mucus-high groups, with 24.1% as the threshold. In the training cohort, patients with mucus-high had unfavorable outcomes (hazard ratio for high vs. low 1.88, 95% confidence interval 1.18–2.99, P = 0.008), with 5-year overall survival rates of 54.8% and 73.7% in mucus-high and mucus-low groups, respectively. The results were confirmed in the validation cohort (2.09, 1.21–3.60, 0.008; 62.8% vs. 79.8%). The prognostic value of MTR was maintained in multivariate analysis for both cohorts. CONCLUSION: The deep learning quantified MTR was an independent prognostic factor in CRC. With the advantages of advanced efficiency and high consistency, our method is suitable for clinical application and promotes precision medicine development. |
format | Online Article Text |
id | pubmed-8982603 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-89826032022-06-10 Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images Zhao, Ke Wu, Lin Huang, Yanqi Yao, Su Xu, Zeyan Lin, Huan Wang, Huihui Liang, Yanting Xu, Yao Chen, Xin Zhao, Minning Peng, Jiaming Huang, Yuli Liang, Changhong Li, Zhenhui Li, Yong Liu, Zaiyi Precis Clin Med Research Article BACKGROUND: In colorectal cancer (CRC), mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype, morphology, and prognosis. However, mucinous components are present in a large number of adenocarcinomas, and the prognostic value of mucus proportion has not been investigated. Artificial intelligence provides a way to quantify mucus proportion on whole-slide images (WSIs) accurately. We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts. METHODS: Deep learning was used to segment WSIs stained with hematoxylin and eosin. Mucus-tumor ratio (MTR) was defined as the proportion of mucinous component in the tumor area. A training cohort (N = 419) and a validation cohort (N = 315) were used to evaluate the prognostic value of MTR. Survival analysis was performed using the Cox proportional hazard model. RESULT: Patients were stratified to mucus-low and mucus-high groups, with 24.1% as the threshold. In the training cohort, patients with mucus-high had unfavorable outcomes (hazard ratio for high vs. low 1.88, 95% confidence interval 1.18–2.99, P = 0.008), with 5-year overall survival rates of 54.8% and 73.7% in mucus-high and mucus-low groups, respectively. The results were confirmed in the validation cohort (2.09, 1.21–3.60, 0.008; 62.8% vs. 79.8%). The prognostic value of MTR was maintained in multivariate analysis for both cohorts. CONCLUSION: The deep learning quantified MTR was an independent prognostic factor in CRC. With the advantages of advanced efficiency and high consistency, our method is suitable for clinical application and promotes precision medicine development. Oxford University Press 2021-01-28 /pmc/articles/PMC8982603/ /pubmed/35693123 http://dx.doi.org/10.1093/pcmedi/pbab002 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the West China School of Medicine & West China Hospital of Sichuan University. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Research Article Zhao, Ke Wu, Lin Huang, Yanqi Yao, Su Xu, Zeyan Lin, Huan Wang, Huihui Liang, Yanting Xu, Yao Chen, Xin Zhao, Minning Peng, Jiaming Huang, Yuli Liang, Changhong Li, Zhenhui Li, Yong Liu, Zaiyi Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images |
title | Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images |
title_full | Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images |
title_fullStr | Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images |
title_full_unstemmed | Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images |
title_short | Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images |
title_sort | deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982603/ https://www.ncbi.nlm.nih.gov/pubmed/35693123 http://dx.doi.org/10.1093/pcmedi/pbab002 |
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