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丁妍, 韩梦雪, 刘月平. AI辅助预估乳腺癌淋巴结转移的研究现状及前景[J]. 四川大学学报(医学版), 2021, 52(2): 162-165. DOI: 10.12182/20210360102
引用本文: 丁妍, 韩梦雪, 刘月平. AI辅助预估乳腺癌淋巴结转移的研究现状及前景[J]. 四川大学学报(医学版), 2021, 52(2): 162-165. DOI: 10.12182/20210360102
DING Yan, HAN Meng-xue, LIU Yue-ping. AI-assisted Prediction of Lymph Node Metastasis of Breast Cancer: Current and Prospective Research[J]. Journal of Sichuan University (Medical Sciences), 2021, 52(2): 162-165. DOI: 10.12182/20210360102
Citation: DING Yan, HAN Meng-xue, LIU Yue-ping. AI-assisted Prediction of Lymph Node Metastasis of Breast Cancer: Current and Prospective Research[J]. Journal of Sichuan University (Medical Sciences), 2021, 52(2): 162-165. DOI: 10.12182/20210360102

AI辅助预估乳腺癌淋巴结转移的研究现状及前景

AI-assisted Prediction of Lymph Node Metastasis of Breast Cancer: Current and Prospective Research

  • 摘要: 人工智能(artificial intelligence,AI)在病理学中最重要的潜在应用之一,是根据形态学特征预测患者的预后和对特定疗法的反应。乳腺癌作为全世界最常见的恶性肿瘤之一,也是导致女性恶性肿瘤死亡的主要原因,是临床关注的焦点。乳腺癌腋窝淋巴结转移是重要的预后因素,能否准确评估腋窝淋巴结转移情况影响临床诊疗决策。目前,基于无创手术的思想,有多项研究已开发出可用于预测乳腺癌前哨淋巴结转移的模型,但是不同预测模型应用的临床和病理参数不同,如何更全面地分析乳腺癌患者的临床和病理数据,并建立更精准的预测模型是未来的发展方向。本文通过阐述AI在病理方面的研究进展以及在乳腺癌中的研究现状,对于如何基于AI辅助有效地预估乳腺癌淋巴结转移、建立更精确有效的深度学习算法展开了深入的思考与展望,从而不断提升乳腺癌的诊治水平。

     

    Abstract: One of the most important application of artificial intelligence (AI) in pathology is prediction, using morphological features, of patient prognosis and response to specific treatments. As one of the most common kinds of malignancies in the world and the crucial important cause of death due to malignant tumor among women, breast cancer has become the center of attention in clinical services. Axillary lymph node metastasis is an important prognostic factor in breast cancer. The accuracy of the assessment of axillary lymph node metastasis bears heavily on clinical diagnosis and treatment. At present, based on the principle of non-invasive procedures, many studies have been done to develop models that can be used to predict sentinel lymph node metastasis of breast cancer. However, different clinical and pathological parameters are used in these predictive models. How to analyze the clinical and pathological data of breast cancer patients in a more comprehensive way and how to establish a prediction model with better precision have become the future direction of development. In this paper, we describe the research progress of AI in pathology and the current status of its use in breast cancer research. We have conducted in-depth reflection and looked into the future of ways to predict effectively breast cancer lymph node metastasis and to establish more accurate and effective deep-learning algorithm based on AI assistance so as to continuously improve the diagnosis and treatment of breast cancer.

     

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