NecroGlobalGCN: Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer methods and programs in biomedicine Pub Date : 2024-09-19 DOI:10.1016/j.cmpb.2024.108435
Boyang Deng , Yu Tian , Qi Zhang , Yangyang Wang , Zhenxin Chai , Qiancheng Ye , Shang Yao , Tingbo Liang , Jingsong Li
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Abstract

Background and Objective

Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality, underscoring the importance of accurate prognostic predictions to improve postoperative survival rates in patients. Although micronecrosis has been shown to have high prognostic value in HCC, its application in clinical prognosis prediction requires specialized knowledge and complex calculations, which poses challenges for clinicians. It would be of interest to develop a model to help clinicians make full use of micronecrosis to assess patient survival.

Methods

To address these challenges, we propose a HCC prognosis prediction model that integrates pathological micronecrosis information through Graph Convolutional Neural Networks (GCN). This approach enables GCN to utilize micronecrosis, which has been shown to be highly correlated with prognosis, thereby significantly enhancing prognostic stratification quality. We developed our model using 3622 slides from 752 patients with primary HCC from the FAH-ZJUMS dataset and conducted internal and external validations on the FAH-ZJUMS and TCGA-LIHC datasets, respectively.

Results

Our method outperformed the baseline by 8.18% in internal validation and 9.02% in external validations. Overall, this paper presents a deep learning research paradigm that integrates HCC micronecrosis, enhancing both the accuracy and interpretability of prognostic predictions, with potential applicability to other pathological prognostic markers.

Conclusions

This study proposes a composite GCN prognostic model that integrates information on HCC micronecrosis, collecting large dataset of HCC histopathological images. This approach could assist clinicians in analyzing HCC patient survival and precisely locating and visualizing necrotic tissues that affect prognosis. Following the research paradigm outlined in this paper, other prognostic biomarker integration models with GCN could be developed, significantly enhancing the predictive performance and interpretability of prognostic model.
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NecroGlobalGCN:通过图卷积神经网络在HCC预后预测中整合微坏死信息。
背景和目的:肝细胞癌(HCC)在癌症死亡率中排名第四,这说明准确预测预后对提高患者术后生存率的重要性。虽然微坏死已被证明在 HCC 中具有很高的预后价值,但将其应用于临床预后预测需要专业知识和复杂的计算,这给临床医生带来了挑战。开发一个模型来帮助临床医生充分利用微坏死来评估患者的存活率将是一个很有意义的事情:为了应对这些挑战,我们提出了一种通过图卷积神经网络(GCN)整合病理微坏死信息的 HCC 预后预测模型。这种方法使 GCN 能够利用已被证明与预后高度相关的微坏死,从而显著提高预后分层的质量。我们使用来自FAH-ZJUMS数据集的752名原发性HCC患者的3622张切片开发了我们的模型,并分别在FAH-ZJUMS和TCGA-LIHC数据集上进行了内部和外部验证:在内部验证和外部验证中,我们的方法分别比基线方法高出 8.18% 和 9.02%。总之,本文提出了一种整合 HCC 微坏死的深度学习研究范式,提高了预后预测的准确性和可解释性,并有可能适用于其他病理预后标志物:本研究提出了一种复合 GCN 预后模型,该模型整合了 HCC 微坏死信息,收集了大量 HCC 组织病理学图像数据集。这种方法可以帮助临床医生分析 HCC 患者的存活率,并精确定位和观察影响预后的坏死组织。按照本文概述的研究范例,还可以开发出其他与 GCN 相结合的预后生物标志物模型,从而大大提高预后模型的预测性能和可解释性。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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