自从引入到肿瘤学领域以来，深度学习（DL）已经对临床发现和生物标志物预测产生了影响。肿瘤学中的DL驱动的发现和预测基于各种生物数据，如基因组学、蛋白质组学和成像数据。DL基于计算框架可以预测基因变异对基因表达的影响，以及基于氨基酸序列的蛋白质结构。此外，DL算法可以从几种空间“组学”技术（如空间转录组学和空间蛋白质组学）中捕获有价值的生物机制信息。在本文中，我们回顾了人工智能（AI）与空间组学技术相结合在肿瘤学中的影响，重点关注DL及其在生物医学图像分析中的应用，包括细胞分割、细胞表型识别、癌症预测和治疗预测。我们强调使用高度多重成像（空间蛋白质组学数据）相比单染色的传统组织病理学图像的优势，前者可以提供深入的机制见解，即使借助可解释的AI也无法获得。此外，我们还为读者提供了DL-based 管道在预处理高度多重成像（细胞分割、细胞类型注释）中的优缺点。因此，这篇综述也引导读者选择最适合其数据的DL-based管道。总之，DL在与高度多重组织成像数据等技术相结合时仍然被确认为发现新的生物机制的重要工具。在与传统医学数据保持平衡的情况下，它在临床工作中的作用将变得更加重要，为肿瘤学的诊断和预后提供支持，提升临床决策，改善患者护理质量。版权所有©2023 Zidane, Makky, Bruhns, Rochwarger, Babaei, Claassen和Schürch.

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.Copyright © 2023 Zidane, Makky, Bruhns, Rochwarger, Babaei, Claassen and Schürch.