Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH.
Maimonides Medical Center, Department of Internal Medicine, Brooklyn, NY.
JCO Clin Cancer Inform. 2020 Nov;4:1039-1050. doi: 10.1200/CCI.20.00110.
Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conjunction with radiographic imaging techniques, have been linked to outcome, progression, and survival for a number of cancers. The gold standard of staging in oncology has been the TNM (tumor-node-metastasis) staging system. Though histopathological grading has shown prognostic significance, it is subjective and limited by interobserver variability even among experienced surgical pathologists. Recently, artificial intelligence (AI) approaches have been applied to pathology images toward diagnostic-, prognostic-, and treatment prediction-related tasks in cancer. AI approaches have the potential to overcome the limitations of conventional TNM staging and tumor grading approaches, providing a direct prognostic prediction of disease outcome independent of tumor stage and grade. Broadly speaking, these AI approaches involve extracting patterns from images that are then compared against previously defined disease signatures. These patterns are typically categorized as either (1) handcrafted, which involve domain-inspired attributes, such as nuclear shape, or (2) deep learning (DL)-based representations, which tend to be more abstract. DL approaches have particularly gained considerable popularity because of the minimal domain knowledge needed for training, mostly only requiring annotated examples corresponding to the categories of interest. In this article, we discuss AI approaches for digital pathology, especially as they relate to disease prognosis, prediction of genomic and molecular alterations in the tumor, and prediction of treatment response in oncology. We also discuss some of the potential challenges with validation, interpretability, and reimbursement that must be addressed before widespread clinical deployment. The article concludes with a brief discussion of potential future opportunities in the field of AI for digital pathology and oncology.
肿瘤的分期和分级，通过病理学家结合影像学技术评估病理图像进行评估，与多种癌症的结果、进展和生存有关。肿瘤学分期的金标准一直是 TNM（肿瘤-淋巴结-转移）分期系统。虽然组织病理学分级已经显示出预后意义，但它是主观的，并且即使在经验丰富的外科病理学家之间也受到观察者间变异性的限制。最近，人工智能 (AI) 方法已应用于病理图像，以实现癌症相关的诊断、预后和治疗预测任务。AI 方法有可能克服传统 TNM 分期和肿瘤分级方法的局限性，提供独立于肿瘤分期和分级的疾病结果的直接预后预测。广义而言，这些 AI 方法涉及从图像中提取模式，然后将这些模式与先前定义的疾病特征进行比较。这些模式通常分为（1）手工制作的，涉及受领域启发的属性，例如核形状，或（2）基于深度学习 (DL) 的表示，这些表示往往更抽象。DL 方法特别受到欢迎，因为它们在训练方面所需的领域知识很少，只需要与感兴趣的类别相对应的标注示例。在本文中，我们讨论了数字病理学中的 AI 方法，特别是它们与疾病预后、肿瘤中基因组和分子改变的预测以及肿瘤学中治疗反应的预测的关系。我们还讨论了在广泛临床应用之前必须解决的验证、可解释性和报销方面的一些潜在挑战。文章最后简要讨论了数字病理学和肿瘤学领域人工智能的潜在未来机遇。
