Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
Nat Rev Clin Oncol. 2019 Nov;16(11):703-715. doi: 10.1038/s41571-019-0252-y. Epub 2019 Aug 9.
In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and 'hand-crafted' feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.
在过去的十年中，精准肿瘤学的进展导致了对预测性检测的需求增加，这些检测能够选择和分层治疗患者。癌症、基质和免疫细胞之间相互作用的信号和转录网络的巨大差异，使得基于单个基因或蛋白质开发功能相关的生物标志物变得复杂。然而，这些复杂过程的结果可以在染色组织标本的形态特征中独特地捕捉到。组织全玻片图像数字化的可能性导致了人工智能 (AI) 和机器学习工具在数字病理学中的出现，这些工具能够挖掘亚视觉形态表型，并最终改善患者管理。在本观点中，我们批判性地评估了数字病理学中的各种基于 AI 的计算方法，重点是深度学习网络和“手工制作”的基于特征的方法。我们旨在为将人工智能和机器学习工具纳入临床肿瘤学提供一个广泛的框架，重点是生物标志物的开发。我们讨论了与使用人工智能相关的一些挑战，包括对精心策划的验证数据集、监管批准和公平报销策略的需求。最后，我们提出了精准肿瘤学的一些潜在未来机会。
