癌细胞迁移驱动的转移是癌症相关死亡的主要原因。它涉及细胞骨架组织的显着变化，其中包括肌动蛋白微丝和波形蛋白中间丝。了解这些细丝如何将细胞从正常细胞转变为侵袭性细胞，可以提供可用于改善癌症诊断和治疗的见解。我们开发了一种计算、透明、大规模和基于成像的管道，可以根据细胞中肌动蛋白和波形蛋白丝的空间组织区分正常人类细胞及其同基因匹配、致癌转化、侵袭和转移的对应细胞细胞质。由于这些亚细胞结构的复杂性，手动注释对于自动化来说并非易事。我们使用了既定的深度学习方法和新的多注意力通道架构。为了确保网络的高水平可解释性（这对于应用领域至关重要），我们开发了一种可解释的全局可解释方法，将总细胞图像的加权几何平均值与其本地 GradCam 分数相关联。这些方法提供了对细胞骨架的不同成分如何促进转移的详细、客观和可测量的理解，这些见解可用于未来开发新型诊断工具，例如用于数字病理学的纳米级、基于波形蛋白丝的生物标志物，以及可以显着提高患者生存率的新疗法。版权所有 © 2024。由 Elsevier Ltd 出版。

Metastasis driven by cancer cell migration is the leading cause of cancer-related deaths. It involves significant changes in the organization of the cytoskeleton, which includes the actin microfilaments and the vimentin intermediate filaments. Understanding how these filament change cells from normal to invasive offers insights that can be used to improve cancer diagnosis and therapy. We have developed a computational, transparent, large-scale and imaging-based pipeline, that can distinguish between normal human cells and their isogenically matched, oncogenically transformed, invasive and metastasizing counterparts, based on the spatial organization of actin and vimentin filaments in the cell cytoplasm. Due to the intricacy of these subcellular structures, manual annotation is not trivial to automate. We used established deep learning methods and our new multi-attention channel architecture. To ensure a high level of interpretability of the network, which is crucial for the application area, we developed an interpretable global explainable approach correlating the weighted geometric mean of the total cell images and their local GradCam scores. The methods offer detailed, objective and measurable understanding of how different components of the cytoskeleton contribute to metastasis, insights that can be used for future development of novel diagnostic tools, such as a nanometer level, vimentin filament-based biomarker for digital pathology, and for new treatments that significantly can increase patient survival.Copyright © 2024. Published by Elsevier Ltd.