González-Suárez Susana, Grao Roca María, Vivas Juan-Camilo, Jauregui Alberto
Department of Surgery, Universitat Autònoma de Barcelona, Barcelona, Spain.
Department of Anesthesia and Intensive Care, University Hospital Vall d'Hebron, Barcelona, Spain.
Front Surg. 2025 May 20;12:1567390. doi: 10.3389/fsurg.2025.1567390. eCollection 2025.
The aim of this study was to show the efficacy described in the scientific literature of lung ultrasound (LU) during video-assisted thoracic surgery (VATS) to determine the location and characterization of pulmonary nodules. The results showed that intraoperative LU is especially useful in localizing lung lesions by VATS with a sensitivity close to 100%. It was also shown to be useful in planning very precise lung resections, reducing the resection of healthy lung tissue. Although general criteria could be established for the degree of benignity/malignancy of lung lesions based on the ultrasound patterns obtained, the great variability observed in these patterns does not offer sufficient guarantees to make a reliable diagnosis. In this sense, the application of Doppler ultrasound or the utilization of Intraoperative Contrast-Enhanced Ultrasound (Io-CEUS) in a completely collapsed lung can be advantageous for the accurate localization and characterization of pulmonary lesions. Even so, at present, definitive confirmation of the nature of lung lesions usually requires biopsy and histopathological study. The development of artificial intelligence algorithms that integrate the results of histological analyses with various types of ultrasonographic patterns (based on pulmonary aeration obtained, as well as Doppler and Io-CEUS records) will likely represent the future of ultrasonographic differentiation of these lesions.
本研究的目的是展示科学文献中所描述的在电视辅助胸腔镜手术(VATS)期间肺部超声(LU)在确定肺结节位置及特征方面的功效。结果表明,术中肺部超声在通过VATS定位肺部病变方面特别有用,其敏感性接近100%。它还被证明在规划非常精确的肺切除手术、减少健康肺组织的切除方面很有用。尽管基于所获得的超声图像可以为肺病变的良恶性程度建立一般标准,但在这些图像中观察到的巨大变异性并不能提供足够的保证来做出可靠的诊断。从这个意义上说,在完全萎陷的肺中应用多普勒超声或术中对比增强超声(Io-CEUS)可能有利于肺病变的准确定位和特征描述。即便如此,目前肺病变性质的最终确认通常仍需要活检和组织病理学研究。整合组织学分析结果与各种类型超声图像(基于所获得的肺通气情况以及多普勒和Io-CEUS记录)的人工智能算法的开发可能代表着这些病变超声鉴别诊断的未来。