Virtual multiplexed immunofluorescence staining from non-antibody-stained fluorescence imaging for gastric cancer prognosis.

IF 9.7 1区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL EBioMedicine Pub Date : 2024-09-01 Epub Date: 2024-08-17 DOI:10.1016/j.ebiom.2024.105287

Zixia Zhou, Yuming Jiang, Zepang Sun, Taojun Zhang, Wanying Feng, Guoxin Li, Ruijiang Li, Lei Xing

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Abstract

Background: Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, such as disease diagnosis, biological research, and drug development. However, these techniques are often hindered by high time and cost requirements.

Methods: Here we present a Multimodal-Attention-based virtual mIF Staining (MAS) system that utilises a deep learning model to extract potential antibody-related features from dual-modal non-antibody-stained fluorescence imaging, specifically autofluorescence (AF) and DAPI imaging. The MAS system simultaneously generates predictions of mIF with multiple survival-associated biomarkers in gastric cancer using self- and multi-attention learning mechanisms.

Findings: Experimental results with 180 pathological slides from 94 patients with gastric cancer demonstrate the efficiency and consistent performance of the MAS system in both cancer and noncancer gastric tissues. Furthermore, we showcase the prognostic accuracy of the virtual mIF images of seven gastric cancer related biomarkers, including CD3, CD20, FOXP3, PD1, CD8, CD163, and PD-L1, which is comparable to those obtained from the standard mIF staining.

Interpretation: The MAS system rapidly generates reliable multiplexed staining, greatly reducing the cost of mIF and improving clinical workflow.

Funding: Stanford 2022 HAI Seed Grant; National Institutes of Health 1R01CA256890.

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利用非抗体染色荧光成像进行虚拟多重免疫荧光染色，用于胃癌预后分析。

背景：多重免疫荧光（mIF）染色技术（如 CODEX 和 MIBI）在疾病诊断、生物研究和药物开发等多个领域具有重要的临床价值。方法：我们在此介绍一种基于多模态-注意力的虚拟 mIF 染色（MAS）系统，该系统利用深度学习模型从双模态非抗体染色荧光成像（特别是自发荧光（AF）和 DAPI 成像）中提取潜在的抗体相关特征。MAS 系统利用自我和多注意学习机制，同时生成胃癌 mIF 与多个生存相关生物标记物的预测结果：研究结果：对来自 94 名胃癌患者的 180 张病理切片进行的实验结果表明，MAS 系统在癌症和非癌症胃组织中均表现出高效和一致的性能。此外，我们还展示了虚拟 mIF 图像对 CD3、CD20、FOXP3、PD1、CD8、CD163 和 PD-L1 等七种胃癌相关生物标志物的预后准确性，其准确性与标准 mIF 染色的准确性相当：MAS系统可快速生成可靠的多重染色，大大降低了mIF的成本，改善了临床工作流程：斯坦福大学 2022 HAI 种子基金；美国国立卫生研究院 1R01CA256890。

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来源期刊

EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology

CiteScore

17.70

自引率

0.90%

发文量

579

审稿时长

5 weeks

期刊介绍： eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.