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.
早期乳腺癌的有效风险评估对于临床决策至关重要，然而风险类别的界定标准尚未达成共识。本文探讨了风险分层方法的最新进展，涵盖组织病理学、免疫组织化学和分子生物标志物，以及前沿的人工智能技术。通过机器学习、深度学习和卷积神经网络的应用，人工智能正在重塑复发风险的预测算法，从而革新诊断准确性和治疗规划。除检测功能外，人工智能还延伸至组织学分型、分级、淋巴结评估及分子特征识别等领域，推动个体化治疗决策。随着癌症发病率上升，运用人工智能加速临床实践突破显得尤为重要，这将使患者和医疗系统共同受益。但需认识到，尽管人工智能提供强大的自动化分析工具，其在患者照护中仍缺乏人类病理学家所具有的细微洞察力、临床情境理解及伦理考量。因此，人工智能与临床实践的成功融合需要医学专家与计算病理学家的协同合作，以实现患者预后的最优化。
Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence
肿瘤(癌症)患者之家