GCN-Based Risk Prediction for Necrosis Slide of Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is one of the most common cancers in the world which ranks fourth in cancer deaths. Primary pathological necrosis is an effective prognostic indicator for hepatocellular carcinoma. We propose a GCN-based approach that mimics the pathologist's perspective for global assessment of necrosis tissue distribution to analyze patient survival. Specifically, we introduced a graph convolutional neural network to construct a spatial map with necrotic tissue and tumor tissue as graph nodes, aiming to mine the contextual information between necrotic tissue in pathological sections. We used 1381 slides from 303 patients from the First Affiliated Hospital of Zhejiang University School to train the model and used TCGA-LIHC for external validation. The C-index of our method outperforms the baseline by about 4.45%, which proves that the information about the spatial distribution of necrosis learned by GCN is meaningful for guiding patient prognosis.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:310 |
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| Enthalten in: |
Studies in health technology and informatics - 310(2024) vom: 01. März, Seite 1579-1583 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Deng, Boyang [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 04.03.2024 Date Revised 04.03.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.3233/SHTI231328 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369167716 |
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| 520 | |a Hepatocellular carcinoma (HCC) is one of the most common cancers in the world which ranks fourth in cancer deaths. Primary pathological necrosis is an effective prognostic indicator for hepatocellular carcinoma. We propose a GCN-based approach that mimics the pathologist's perspective for global assessment of necrosis tissue distribution to analyze patient survival. Specifically, we introduced a graph convolutional neural network to construct a spatial map with necrotic tissue and tumor tissue as graph nodes, aiming to mine the contextual information between necrotic tissue in pathological sections. We used 1381 slides from 303 patients from the First Affiliated Hospital of Zhejiang University School to train the model and used TCGA-LIHC for external validation. The C-index of our method outperforms the baseline by about 4.45%, which proves that the information about the spatial distribution of necrosis learned by GCN is meaningful for guiding patient prognosis | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a GCN | |
| 650 | 4 | |a Hepatocellular carcinoma | |
| 650 | 4 | |a Necrosis | |
| 700 | 1 | |a Tian, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Qiancheng |e verfasserin |4 aut | |
| 700 | 1 | |a Chai, Zhenxing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Tianshu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Qi |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Tingbo |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jingsong |e verfasserin |4 aut | |
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