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Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. ​

文献信息 ​

DOI10.1038/s41571-019-0252-y
PMID31399699
期刊Nature reviews. Clinical oncology
影响因子82.2
JCR 分区Q1
发表年份2019
被引次数605
关键词人工智能, 数字病理, 精准肿瘤学, 生物标志物, 深度学习
文献类型Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S., Review
ISSN1759-4774
页码703-715
期号16(11)
作者Kaustav Bera, Kurt A Schalper, David L Rimm, Vamsidhar Velcheti, Anant Madabhushi

一句话小结 ​

本研究探讨了人工智能和机器学习在数字病理学中的应用，强调了其在精准肿瘤学中开发生物标志物的重要性，并批判性评估了不同计算方法的优势与挑战。研究指出，尽管存在数据集验证、监管批准和报销策略等困难，AI技术的进步有望进一步提升患者管理和治疗选择。

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人工智能 · 数字病理 · 精准肿瘤学 · 生物标志物 · 深度学习

摘要 ​

在过去十年中，精准肿瘤学的进展导致对预测性检测的需求增加，这些检测能够帮助选择和分层患者以进行治疗。癌症、基质细胞和免疫细胞之间的信号传导和转录网络的巨大差异，使得基于单一基因或蛋白质开发功能相关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得到独特捕捉。全幻灯片图像数字化的可能性促成了人工智能（AI）和机器学习工具在数字病理学中的出现，这使得可以挖掘亚视觉形态表型，并可能最终改善患者管理。在这篇观点文章中，我们批判性地评估了各种基于AI的数字病理学计算方法，重点讨论深度神经网络和“手工制作”特征基础的方法。我们的目标是为将AI和机器学习工具纳入临床肿瘤学提供一个广泛的框架，特别强调生物标志物的开发。我们讨论了与使用AI相关的一些挑战，包括对良好策划的验证数据集、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潜在未来机会。

英文摘要 ​

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.

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主要研究问题 ​

  1. 在数字病理学中，AI工具如何具体提高对不同癌症类型的诊断精度？
  2. 目前有哪些成功的案例展示了AI在生物标志物开发中的应用？
  3. 在AI和机器学习的应用中，如何解决数据集的验证和监管审批问题？
  4. AI在数字病理学中的应用如何影响临床决策和患者管理的流程？
  5. 有哪些新兴的AI技术可能会改变未来精准肿瘤学的研究和实践？

核心洞察 ​

研究背景和目的 ​

随着精准肿瘤学的进展，临床对能够支持治疗选择和患者分层的预测检测需求不断增加。癌症、基质细胞和免疫细胞之间的信号传递和转录网络的复杂性，使得基于单一基因或蛋白质开发功能相关生物标志物变得困难。因此，利用人工智能（AI）和机器学习（ML）工具分析数字病理学图像，以挖掘组织切片的形态特征，成为提升患者管理的重要途径。

主要方法/材料/实验设计 ​

研究主要采用了AI驱动的数字病理学图像分析方法，重点分析深度神经网络和手工特征提取方法。以下是主要技术路线的流程图：

Mermaid diagram

关键结果和发现 ​

  1. AI在数字病理学中的应用：AI方法已成功应用于图像处理和分类任务，包括低级别的对象识别和高级别的疾病诊断与预后预测。
  2. 深度学习与手工特征工程的比较：深度学习方法不依赖于人工特征设计，能够直接从原始数据中学习表示；而手工特征方法通常需要病理学家提供领域知识。
  3. 临床应用效果：多项研究表明，AI方法的准确性与专家病理学家相当，且在与标准协议结合使用时，能进一步提高人类阅读者的表现。

主要结论/意义/创新性 ​

本研究表明，AI驱动的数字病理学工具有潜力在癌症诊断和预后中提供客观、可重复的结果，能够整合多维度的生物标志物信息。这种方法不仅提升了诊断准确性，也为精准医疗提供了新的视角和手段。

研究局限性和未来方向 ​

  1. 数据质量和标准化：AI算法的性能高度依赖于输入数据的质量，当前缺乏标准化的验证数据集。
  2. 临床采纳的挑战：包括监管审批、数据隐私和保险报销等问题，阻碍了AI工具的广泛应用。
  3. 未来方向：未来研究应聚焦于结合分子和形态特征的综合生物标志物开发，利用3D成像技术与AI分析相结合，提升肿瘤分析的全面性和准确性。

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  2. Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy. - Kuiyuan Liu;Weixiong Xia;Mengyun Qiang;Xi Chen;Jia Liu;Xiang Guo;Xing Lv - Cancer medicine (2020)
  3. Opportunities for Artificial Intelligence in Advancing Precision Medicine. - Fabian V Filipp - Current genetic medicine reports (2019)
  4. Introduction to digital pathology and computer-aided pathology. - Soojeong Nam;Yosep Chong;Chan Kwon Jung;Tae-Yeong Kwak;Ji Youl Lee;Jihwan Park;Mi Jung Rho;Heounjeong Go - Journal of pathology and translational medicine (2020)
  5. Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning. - Saima Rathore;Tamim Niazi;Muhammad Aksam Iftikhar;Ahmad Chaddad - Cancers (2020)
  6. Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. - Hersh K Bhargava;Patrick Leo;Robin Elliott;Andrew Janowczyk;Jon Whitney;Sanjay Gupta;Pingfu Fu;Kosj Yamoah;Francesca Khani;Brian D Robinson;Timothy R Rebbeck;Michael Feldman;Priti Lal;Anant Madabhushi - Clinical cancer research : an official journal of the American Association for Cancer Research (2020)
  7. Image-guided tumor surgery: The emerging role of nanotechnology. - Nicholas E Wojtynek;Aaron M Mohs - Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology (2020)
  8. Editorial: Digital Interventions in Mental Health: Current Status and Future Directions. - Elias Aboujaoude;Lina Gega;Michelle B Parish;Donald M Hilty - Frontiers in psychiatry (2020)
  9. Artificial intelligence driven next-generation renal histomorphometry. - Briana A Santo;Avi Z Rosenberg;Pinaki Sarder - Current opinion in nephrology and hypertension (2020)
  10. Emerging role of deep learning-based artificial intelligence in tumor pathology. - Yahui Jiang;Meng Yang;Shuhao Wang;Xiangchun Li;Yan Sun - Cancer communications (London, England) (2020)

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