PDF(Pubmed)
Abstract：
Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.
摘要：
早期乳腺癌的有效风险评估对于明智的临床决策至关重要。然而，关于定义风险类别的共识仍然具有挑战性。本文探讨了风险分层中不断发展的方法，包括组织病理学，免疫组织化学，和分子生物标志物以及尖端的人工智能(AI)技术。利用机器学习，深度学习,和卷积神经网络，人工智能正在重塑复发风险的预测算法，从而彻底改变诊断准确性和治疗计划。超出检测范围，人工智能应用扩展到组织学亚型，分级,淋巴结评估,和分子特征识别，促进个性化治疗决策。随着癌症发病率的上升，实施人工智能以加快临床实践的突破至关重要，有利于患者和医疗保健提供者。然而,重要的是要认识到，虽然人工智能提供了强大的自动化和分析工具，它缺乏细致入微的理解，临床背景，以及人类病理学家在病人护理中固有的伦理考虑。因此，人工智能成功整合到临床实践中需要医学专家和计算病理学家之间的合作努力，以优化患者的结果。
