Tang Fei, Zha Xian-Kui, Ye Wei, Wang Yue-Ming, Wu Ying-Feng, Wang Li-Na, Lyu Li-Ping, Lyu Xiao-Mei
Respiratory and Critical Care Medicine Department and Endoscopic Diagnosis and Treatment Center, Anhui Chest Hospital, Hefei, 230022, Anhui Province, China.
BMC Pulm Med. 2025 Jul 2;25(1):303. doi: 10.1186/s12890-025-03760-4.
Endobronchial ultrasound (EBUS) is a widely used imaging modality for evaluating thoracic lymph nodes (LNs), particularly in the staging of lung cancer. Artificial intelligence (AI)-assisted EBUS has emerged as a promising tool to enhance diagnostic accuracy. However, its effectiveness in differentiating benign from malignant thoracic LNs remains uncertain. This meta-analysis aimed to evaluate the diagnostic performance of AI-assisted EBUS compared to the pathological reference standards.
A systematic search was conducted across PubMed, Embase, and Web of Science for studies assessing AI-assisted EBUS in differentiating benign and malignant thoracic LNs. The reference standard included pathological confirmation via EBUS-guided transbronchial needle aspiration, surgical resection, or other histological/cytological validation methods. Sensitivity, specificity, diagnostic likelihood ratios, and diagnostic odds ratio (OR) were pooled using a random-effects model. The area under the receiver operating characteristic curve (AUROC) was summarized to evaluate diagnostic accuracy. Subgroup analyses were conducted by study design, lymph node location, and AI model type.
Twelve studies with a total of 6,090 thoracic LNs were included. AI-assisted EBUS showed a pooled sensitivity of 0.75 (95% confidence interval [CI]: 0.60-0.86, I² = 97%) and specificity of 0.88 (95% CI: 0.83-0.92, I² = 96%). The positive and negative likelihood ratios were 6.34 (95% CI: 4.41-9.08) and 0.28 (95% CI: 0.17-0.47), respectively. The pooled diagnostic OR was 22.38 (95% CI: 11.03-45.38), and the AUROC was 0.90 (95% CI: 0.88-0.93). The subgroup analysis showed higher sensitivity but lower specificity in retrospective studies compared to prospective ones (sensitivity: 0.87 vs. 0.42; specificity: 0.80 vs. 0.93; both p < 0.001). No significant differences were found by lymph node location or AI model type.
AI-assisted EBUS shows promise in differentiating benign from malignant thoracic LNs, particularly those with high specificity. However, substantial heterogeneity and moderate sensitivity highlight the need for cautious interpretation and further validation.
PROSPERO CRD42025637964.
支气管内超声（EBUS）是一种广泛用于评估胸部淋巴结（LN）的成像方式，尤其是在肺癌分期中。人工智能（AI）辅助的EBUS已成为提高诊断准确性的一种有前景的工具。然而，其在区分良性与恶性胸部LN方面的有效性仍不确定。本荟萃分析旨在评估与病理参考标准相比，AI辅助EBUS的诊断性能。
在PubMed、Embase和Web of Science上进行系统检索，以查找评估AI辅助EBUS区分良性和恶性胸部LN的研究。参考标准包括通过EBUS引导的经支气管针吸活检、手术切除或其他组织学/细胞学验证方法进行的病理确认。使用随机效应模型汇总敏感性、特异性、诊断似然比和诊断比值比（OR）。汇总受试者工作特征曲线下面积（AUROC）以评估诊断准确性。按研究设计、淋巴结位置和AI模型类型进行亚组分析。
纳入了12项研究，共6090个胸部LN。AI辅助EBUS的汇总敏感性为0.75（95%置信区间[CI]：0.60-0.86，I² = 97%），特异性为0.88（95%CI：0.83-0.92，I² = 96%）。阳性和阴性似然比分别为6.34（95%CI：4.41-9.08）和0.28（95%CI：0.17-0.47）。汇总诊断OR为22.38（95%CI：11.03-45.38），AUROC为0.90（95%CI：0.88-0.93）。亚组分析显示，与前瞻性研究相比，回顾性研究的敏感性较高但特异性较低（敏感性：0.87对0.42；特异性：0.80对0.93；均p < 0.001）。按淋巴结位置或AI模型类型未发现显著差异。
AI辅助EBUS在区分良性与恶性胸部LN方面显示出前景，尤其是那些具有高特异性的LN。然而，大量的异质性和中等的敏感性凸显了谨慎解释和进一步验证的必要性。
PROSPERO CRD42025637964。
