人工智能在头颈部鳞状细胞癌淋巴结转移的病理研究进展
Advances in the pathological study of artificial intelligence in the lymph node metastasis of head and neck squamous cell carcinoma
谢玉林 1雷大鹏1
作者信息
- 1. 山东大学齐鲁医院 耳鼻咽喉科/国家卫生健康委员会耳鼻咽喉科学重点实验室,山东 济南 250012
- 折叠
摘要
人工智能(artificial intelligence,AI)在医学领域发展迅速,广泛应用于疾病的诊断及预后评价.头颈癌是全球常见恶性肿瘤之一,其中大部分为鳞状细胞癌,头颈部鳞状细胞癌(head and neck squamous cell carcinoma,HNSCC)颈部淋巴结转移是重要的预后因素,能否准确评估颈部淋巴结转移情况影响临床决策.目前许多研究已开发出预测HNSCC颈部淋巴结转移的模型,但不同模型构建时应用的临床、病理参数不同,如何更全面地分析HNSCC患者的临床、病理数据,并建立更精准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HNSCC中的研究现状,对于如何运用AI有效地评估HNSCC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HNSCC的诊疗水平.
Abstract
Artificial intelligence(AI)has developed rapidly in the field of medicine and is widely used in the diagnosis and progno-sis evaluation of diseases.Head and neck cancer is one of the most common malignant tumors in the world,most of which are squa-mous cell carcinoma.Cervical lymph node metastasis of head and neck squamous cell carcinoma(HNSCC)is an important prognos-tic factor.The accuracy of the assessment of cervical lymph node metastasis is highly dependent on clinical diagnosis and treatment.At present,many studies have been conducted to develop models that can be used to predict cervical lymph node metastasis of HNSCC,and different clinical and pathological parameters were used in these predictive models.In order to progress development,we need to determine the optimum method to analyze the clinical and pathological data of HNSCC patients in a more comprehensive manner,as well as establish a prediction model with better precision.In this paper,we describe the progress of AI in the field of pathology and discuss the current status of its use in HNSCC research.Additionally,we have conducted an in-depth consideration and prospect on building an accurate and efficient AI prediction model for HNSCC lymph node metastasis to continuously improve the diagnosis and treatment of HNSCC.
关键词
人工智能/头颈鳞癌/淋巴结转移Key words
Artificial Intelligence/Head and neck squamous cell carcinoma/Lymph node metastasis引用本文复制引用
基金项目
国家自然科学基金面上项目(82071918)
出版年
2024
NETL
NSTL
万方数据