Current status of research on the application of artificial intelligence in lung nodule prediction modeling

doi:10.3877/cma.j.issn.2095-8773.2025.01.05

Abstract

Abstract:

Lung nodules are a common chest imaging manifestation，which has received extensive attention from the clinic and society.In recent years， artificial intelligence （AI） technology based on computed tomography （CT） images has matured and has been widely applied in the classification of lung nodule detection，pathologic typing，malignancy degree and gene mutation prediction.The aim of this paper is to sort out and analyze the relevant studies of AI in the field of lung nodule diagnosis and treatment.

Key words: Pulmonary nodules, Lung adenocarcinoma, Computed tomography images, Artificial intelligence, Prediction model

Yujie Ma, Yuhe You, Zhe Piao, Hongsheng Xue, Wenjun Cao, Zhen Wang, Zhilong Zhao. Current status of research on the application of artificial intelligence in lung nodule prediction modeling[J]. Chinese Journal of Thoracic Surgery(Electronic Edition), 2025, 12(01): 39-48.

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年份	作者	应用领域	模型表现（基于深度学习）		研究方法	其他模型表现
年份	作者	应用领域	准确率，%	曲线下面积	研究方法	准确率，%	曲线下面积
2021	Wang 等^［34］	浸润程度	90.6	0.93	影像组学模型	88.2	/
		病理亚型	80.6	0.80 ～ 0.91	影像组学模型	70.5	/
2023	Ma 等^［64］	淋巴结转移	/	0.961	临床语义模型/影像组学特征	/	0.85/0.87
2024	Lin 等^［45］	气腔播散	74.0	0.82	影像组学模型	68.0	0.78
2024	Liu 等^［51］	脉管侵犯	72.0	0.77	临床模型	63.0	0.71
2024	Lin 等^［58］	胸膜侵犯	65.0	0.75	临床模型	60.0	0.74

年份	作者	应用领域	模型表现（基于深度学习）		研究方法	其他模型表现
年份	作者	应用领域	准确率，%	曲线下面积	研究方法	准确率，%	曲线下面积
2021	Wang 等^［34］	浸润程度	90.6	0.93	影像组学模型	88.2	/
		病理亚型	80.6	0.80 ～ 0.91	影像组学模型	70.5	/
2023	Ma 等^［64］	淋巴结转移	/	0.961	临床语义模型/影像组学特征	/	0.85/0.87
2024	Lin 等^［45］	气腔播散	74.0	0.82	影像组学模型	68.0	0.78
2024	Liu 等^［51］	脉管侵犯	72.0	0.77	临床模型	63.0	0.71
2024	Lin 等^［58］	胸膜侵犯	65.0	0.75	临床模型	60.0	0.74

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