Deep Learning Analysis of the Adipose Tissue and the Prediction of Prognosis in Colorectal Cancer
Copyright © 2022 Lin, Qi, Li, Guan, Imyanitov, Mitiushkina, Cheng, Liu, Wang, Lyu, Zhang and Luo..
Research has shown that the lipid microenvironment surrounding colorectal cancer (CRC) is closely associated with the occurrence, development, and metastasis of CRC. According to pathological images from the National Center for Tumor diseases (NCT), the University Medical Center Mannheim (UMM) database and the ImageNet data set, a model called VGG19 was pre-trained. A deep convolutional neural network (CNN), VGG19CRC, was trained by the migration learning method. According to the VGG19CRC model, adipose tissue scores were calculated for TCGA-CRC hematoxylin and eosin (H&E) images and images from patients at Zhujiang Hospital of Southern Medical University and First People's Hospital of Chenzhou. Kaplan-Meier (KM) analysis was used to compare the overall survival (OS) of patients. The XCell and MCP-Counter algorithms were used to evaluate the immune cell scores of the patients. Gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) were used to analyze upregulated and downregulated pathways. In TCGA-CRC, patients with high-adipocytes (high-ADI) CRC had significantly shorter OS times than those with low-ADI CRC. In a validation queue from Zhujiang Hospital of Southern Medical University (Local-CRC1), patients with high-ADI had worse OS than CRC patients with low-ADI. In another validation queue from First People's Hospital of Chenzhou (Local-CRC2), patients with low-ADI CRC had significantly longer OS than patients with high-ADI CRC. We developed a deep convolution network to segment various tissues from pathological H&E images of CRC and automatically quantify ADI. This allowed us to further analyze and predict the survival of CRC patients according to information from their segmented pathological tissue images, such as tissue components and the tumor microenvironment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
Frontiers in nutrition - 9(2022) vom: 17., Seite 869263 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lin, Anqi [VerfasserIn] |
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| Links: |
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| Themen: |
Adipose tissue |
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| Anmerkungen: |
Date Revised 16.07.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fnut.2022.869263 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Research has shown that the lipid microenvironment surrounding colorectal cancer (CRC) is closely associated with the occurrence, development, and metastasis of CRC. According to pathological images from the National Center for Tumor diseases (NCT), the University Medical Center Mannheim (UMM) database and the ImageNet data set, a model called VGG19 was pre-trained. A deep convolutional neural network (CNN), VGG19CRC, was trained by the migration learning method. According to the VGG19CRC model, adipose tissue scores were calculated for TCGA-CRC hematoxylin and eosin (H&E) images and images from patients at Zhujiang Hospital of Southern Medical University and First People's Hospital of Chenzhou. Kaplan-Meier (KM) analysis was used to compare the overall survival (OS) of patients. The XCell and MCP-Counter algorithms were used to evaluate the immune cell scores of the patients. Gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) were used to analyze upregulated and downregulated pathways. In TCGA-CRC, patients with high-adipocytes (high-ADI) CRC had significantly shorter OS times than those with low-ADI CRC. In a validation queue from Zhujiang Hospital of Southern Medical University (Local-CRC1), patients with high-ADI had worse OS than CRC patients with low-ADI. In another validation queue from First People's Hospital of Chenzhou (Local-CRC2), patients with low-ADI CRC had significantly longer OS than patients with high-ADI CRC. We developed a deep convolution network to segment various tissues from pathological H&E images of CRC and automatically quantify ADI. This allowed us to further analyze and predict the survival of CRC patients according to information from their segmented pathological tissue images, such as tissue components and the tumor microenvironment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a adipose tissue | |
| 650 | 4 | |a colorectal cancer | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a hematoxylin and eosin | |
| 650 | 4 | |a prognosis | |
| 700 | 1 | |a Qi, Chang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Mujiao |e verfasserin |4 aut | |
| 700 | 1 | |a Guan, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Imyanitov, Evgeny N |e verfasserin |4 aut | |
| 700 | 1 | |a Mitiushkina, Natalia V |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Quan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Zaoqu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaojun |e verfasserin |4 aut | |
| 700 | 1 | |a Lyu, Qingwen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Peng |e verfasserin |4 aut | |
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