Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.
Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.
复制标题
细胞图神经网络能够精确预测胃癌患者的生存率
DOI：
10.1038/s41698-022-00285-5
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发表时间：
2022-06-23
影响因子：
7.9
通讯作者：
中科院分区：
文献类型：
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作者：
胃癌是全世界最致命的癌症之一。准确的预后对于有效的临床评估和治疗至关重要。肿瘤微环境（TME）的空间模式在概念上指示胃癌患者的分期和进展。通过将多重免疫组织化学 (mIHC) 图像集成和转换为 Cell-Graph，利用 TME 的空间模式，我们提出了一种基于图神经网络的方法，称为 Cell−Graph Signature 或 CGSignature，由人工智能提供支持，用于 TME 的数字分期和胃癌患者生存的精确预测。在本研究中，患者生存预测被制定为二元（短期和长期）或三元（短期、中期和长期）分类任务。大量的基准测试实验表明，CGSignature 具有出色的模型性能，二元和三元分类的接收器操作特征曲线下面积分别为 0.960±0.01 和 0.771±0.024 至 0.904±0.012。此外，Kaplan-Meier 生存分析表明，CGSignature 生成的“数字级”癌症分期在区分二元和三元类别方面具有显着的能力（P 值 < 0.0001），显着优于 AJCC 第 8 版肿瘤淋巴结转移分期系统。 CGSignature 使用从 mIHC 图像中提取的 Cell-Graphs 改进了对 TME 空间模式与患者预后之间联系的评估。我们的研究表明了这种人工智能驱动的数字分期系统在诊断病理学和精准肿瘤学中的可行性和益处。
Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell−Graph Signature or CGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960 ± 0.01, and 0.771 ± 0.024 to 0.904 ± 0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan–Meier survival analysis indicates that the “digital grade” cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value < 0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.
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影响因子：
16.6
作者：
Huang, Yu-Kuan;Wang, Minyu;Boussioutas, Alex
通讯作者：
Boussioutas, Alex
影响因子：
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作者：
Scarselli, Franco;Gori, Marco;Monfardini, Gabriele
通讯作者：
Monfardini, Gabriele
影响因子：
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作者：
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Wang, Ziqiang
影响因子：
168.9
作者：
Bang, Yung-Jue;Kim, Young-Woo;Noh, Sung Hoon
通讯作者：
Noh, Sung Hoon
影响因子：
5.8
作者：
Lababede, Omar;Meziane, Moulay A.
通讯作者：
Meziane, Moulay A.