Dang Chengrun, Qi Zhuang, Xu Tao, Gu Mingkai, Chen Jiajia, Wu Jie, Lin Yuxin, Qi Xin
School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, China.
School of Software, Shandong University, Jinan, China.
Lab Invest. 2025 Apr 28;105(7):104186. doi: 10.1016/j.labinv.2025.104186.
Pathology is the cornerstone of modern cancer care. With the advancement of precision oncology, the demand for histopathologic diagnosis and stratification of patients is increasing as personalized cancer therapy relies on accurate biomarker assessment. Recently, rapid development of whole slide imaging technology has enabled digitalization of traditional histologic slides at high resolution, holding promise to improve both the precision and efficiency of histopathologic evaluation. In particular, deep learning approaches, such as Convolutional Neural Network, Graph Convolutional Network, and Transformer, have shown great promise in enhancing the sensitivity and accuracy of whole slide image (WSI) analysis in cancer pathology because of their ability to handle high-dimensional and complex image data. The integration of deep learning models with WSIs enables us to explore and mine morphologic features beyond the visual perception of pathologists, which can help automate clinical diagnosis, assess histopathologic grade, predict clinical outcomes, and even discover novel morphologic biomarkers. In this review, we present a comprehensive framework for incorporating deep learning with WSIs, highlighting how deep learning-driven WSI analysis advances clinical tasks in cancer care. Furthermore, we critically discuss the opportunities and challenges of translating deep learning-based digital pathology into clinical practice, which should be considered to support personalized treatment of cancer patients.
病理学是现代癌症治疗的基石。随着精准肿瘤学的发展，由于个性化癌症治疗依赖于准确的生物标志物评估，对患者进行组织病理学诊断和分层的需求日益增加。最近，全玻片成像技术的快速发展已能够将传统组织学玻片进行高分辨率数字化，有望提高组织病理学评估的精度和效率。特别是，深度学习方法，如卷积神经网络、图卷积神经网络和变换器，因其能够处理高维复杂图像数据，在提高癌症病理学全玻片图像（WSI）分析的敏感性和准确性方面显示出巨大潜力。深度学习模型与WSI的整合使我们能够探索和挖掘病理学家视觉感知之外的形态学特征，这有助于实现临床诊断自动化、评估组织病理学分级、预测临床结果，甚至发现新的形态学生物标志物。在本综述中，我们提出了一个将深度学习与WSI相结合的综合框架，强调了深度学习驱动的WSI分析如何推动癌症治疗中的临床任务。此外，我们批判性地讨论了将基于深度学习的数字病理学转化为临床实践的机遇和挑战，这些应被视为支持癌症患者个性化治疗的考量因素。
