基于人工智能的CT影像组学在结核病诊断和治疗反应监测中应用的研究进展

doi:10.19982/j.issn.1000-6621.20250149

摘要/Abstract

摘要：

近年来,影像组学模型通过融合临床特征与深度学习技术,可有效区分肺结核与肺癌、非结核分枝杆菌肺病及社区获得性肺炎等,在肺结核鉴别诊断中表现优异,显著优于传统影像评估,尤其可为诊断困难、样本获取受限的肺外结核(如肠结核、淋巴结结核)提供强有力的无创诊断工具。其构建的多模态融合模型不仅在鉴别肠结核与克罗恩病、淋巴结结核与淋巴瘤等方面展现出较高的精度,还在耐药结核病的预测及治疗反应的动态监测中显示出重要的潜力,在结核病诊疗中展现出广阔前景。但受限于数据集质量参差不齐、模型泛化能力有限、临床验证不足等问题,影像组学模型在结核病诊疗中仍面临着严峻挑战。本文通过深入分析文献发现,系统综述了人工智能(artificial intelligence, AI)驱动的计算机断层扫描(computed tomography, CT)影像组学技术在结核病(含肺结核及肺外结核)诊断及治疗反应监测中的最新研究进展与应用价值,聚焦于多模态融合技术与临床落地场景的创新分析,为指引未来的研究方向、进一步推动其在结核病诊疗中的应用与发展、助力结核病精准医疗和防控工作提供借鉴。

关键词: 人工智能, 放射摄影影像解释,计算机辅助, 结核, 诊断,计算机辅助, 综述文献(主题)

Abstract:

In recent years, radiomics models, have effectively distinguished pulmonary tuberculosis from lung cancer, nontuberculous mycobacterial lung disease, community-acquired pneumonia, etc, through integrating clinical features with deep learning technologies. They have shown excellent performance in the differential diagnosis of pulmonary tuberculosis, significantly outperforming traditional imaging evaluations. In particular, they can provide a powerful non-invasive diagnostic tool for extrapulmonary tuberculosis (such as intestinal tuberculosis and lymph node tuberculosis) cases with difficult diagnosis and limited sample acquisition. The constructed multimodal fusion models not only demonstrate high accuracy in differentiating intestinal tuberculosis from Crohn’s disease and lymph node tuberculosis from lymphoma but also show significant potential in predicting drug-resistant tuberculosis and dynamically monitoring treatment responses, presenting broad prospects in the diagnosis and treatment response monitoring of tuberculosis. However, constrained by issues such as uneven dataset quality, limited model generalization ability, and insufficient clinical validation, radiomics models still face severe challenges in the diagnosis and treatment of tuberculosis. Through in-depth literature analysis, this paper systematically reviews the latest research progress and application value of artificial intelligence (AI)-driven computed tomography (CT) radiomics technology in the diagnosis and treatment of tuberculosis (including pulmonary and extrapulmonary tuberculosis). It focuses on the innovative analysis of multimodal fusion technologies and clinical implementation scenarios, aiming to provide references for guiding future research directions, further promoting its application and development in the diagnosis and treatment response monitoring of tuberculosis, and contributing to the precision medicine, prevention, and control of tuberculosis.

Key words: Artificial intelligence, Radiographic image interpretation, computer-assisted, Tuberculosis, Diagnosis, computer-assisted, Review literature as topic

中图分类号:

R445.3
R52

朱庆东, 赵春艳, 谢周华, 宋树林, 宋畅. 基于人工智能的CT影像组学在结核病诊断和治疗反应监测中应用的研究进展[J]. 中国防痨杂志, 2025, 47(8): 1068-1076. doi: 10.19982/j.issn.1000-6621.20250149

Zhu Qingdong, Zhao Chunyan, Xie Zhouhua, Song Shulin, Song Chang. Research progress on the application of artificial intelligence-based CT radiomics in the diagnosis and treatment response monitoring of tuberculosis[J]. Chinese Journal of Antituberculosis, 2025, 47(8): 1068-1076. doi: 10.19982/j.issn.1000-6621.20250149

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