Ding Yan, Han Meng-Xue, Liu Yue-Ping
Department of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China.
Sichuan Da Xue Xue Bao Yi Xue Ban. 2021 Mar;52(2):162-165. doi: 10.12182/20210360102.
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.
人工智能(AI)在病理学中最重要的应用之一是利用形态学特征预测患者的预后以及对特定治疗的反应。乳腺癌作为世界上最常见的恶性肿瘤之一,也是女性因恶性肿瘤死亡的重要原因,已成为临床医疗关注的焦点。腋窝淋巴结转移是乳腺癌重要的预后因素。腋窝淋巴结转移评估的准确性对临床诊断和治疗至关重要。目前,基于非侵入性检查的原则,人们开展了许多研究以开发可用于预测乳腺癌前哨淋巴结转移的模型。然而,这些预测模型所使用的临床和病理参数各不相同。如何更全面地分析乳腺癌患者的临床和病理数据,以及如何建立精度更高的预测模型已成为未来的发展方向。在本文中,我们阐述了AI在病理学方面的研究进展及其在乳腺癌研究中的应用现状。我们进行了深入思考,并展望了有效预测乳腺癌淋巴结转移以及基于AI辅助建立更准确有效的深度学习算法的未来发展方向,以便不断改进乳腺癌的诊断和治疗。