Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis
Abstract Background 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 effecti...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | BMC Pulmonary Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12890-025-03760-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849335394185773056 |
|---|---|
| author | Fei Tang Xian-Kui Zha Wei Ye Yue-Ming Wang Ying-Feng Wu Li-Na Wang Li-Ping Lyu Xiao-Mei Lyu |
| author_facet | Fei Tang Xian-Kui Zha Wei Ye Yue-Ming Wang Ying-Feng Wu Li-Na Wang Li-Ping Lyu Xiao-Mei Lyu |
| author_sort | Fei Tang |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusion 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. Systematic review registration PROSPERO CRD42025637964. |
| format | Article |
| id | doaj-art-19db95f1ba32417bbe4f5e46b5da6a49 |
| institution | Kabale University |
| issn | 1471-2466 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj-art-19db95f1ba32417bbe4f5e46b5da6a492025-08-20T03:45:18ZengBMCBMC Pulmonary Medicine1471-24662025-07-0125111310.1186/s12890-025-03760-4Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysisFei Tang0Xian-Kui Zha1Wei Ye2Yue-Ming Wang3Ying-Feng Wu4Li-Na Wang5Li-Ping Lyu6Xiao-Mei Lyu7Respiratory and Critical Care Medicine Department and Endoscopic Diagnosis and Treatment Center, Anhui Chest HospitalRespiratory and Critical Care Medicine Department and Endoscopic Diagnosis and Treatment Center, Anhui Chest HospitalRespiratory and Critical Care Medicine Department and Endoscopic Diagnosis and Treatment Center, Anhui Chest HospitalRespiratory and Critical Care Medicine Department and Endoscopic Diagnosis and Treatment Center, Anhui Chest HospitalRespiratory and Critical Care Medicine Department and Endoscopic Diagnosis and Treatment Center, Anhui Chest HospitalRespiratory and Critical Care Medicine Department and Endoscopic Diagnosis and Treatment Center, Anhui Chest HospitalRespiratory and Critical Care Medicine Department and Endoscopic Diagnosis and Treatment Center, Anhui Chest HospitalRespiratory and Critical Care Medicine Department and Endoscopic Diagnosis and Treatment Center, Anhui Chest HospitalAbstract Background 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. Methods 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. Results 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. Conclusion 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. Systematic review registration PROSPERO CRD42025637964.https://doi.org/10.1186/s12890-025-03760-4Artificial intelligenceEndobronchial ultrasoundMalignancyMeta-analysisThoracic lymph node |
| spellingShingle | Fei Tang Xian-Kui Zha Wei Ye Yue-Ming Wang Ying-Feng Wu Li-Na Wang Li-Ping Lyu Xiao-Mei Lyu Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis BMC Pulmonary Medicine Artificial intelligence Endobronchial ultrasound Malignancy Meta-analysis Thoracic lymph node |
| title | Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis |
| title_full | Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis |
| title_fullStr | Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis |
| title_full_unstemmed | Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis |
| title_short | Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis |
| title_sort | artificial intelligence assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes a meta analysis |
| topic | Artificial intelligence Endobronchial ultrasound Malignancy Meta-analysis Thoracic lymph node |
| url | https://doi.org/10.1186/s12890-025-03760-4 |
| work_keys_str_mv | AT feitang artificialintelligenceassistedendobronchialultrasoundfordifferentiatingbetweenbenignandmalignantthoraciclymphnodesametaanalysis AT xiankuizha artificialintelligenceassistedendobronchialultrasoundfordifferentiatingbetweenbenignandmalignantthoraciclymphnodesametaanalysis AT weiye artificialintelligenceassistedendobronchialultrasoundfordifferentiatingbetweenbenignandmalignantthoraciclymphnodesametaanalysis AT yuemingwang artificialintelligenceassistedendobronchialultrasoundfordifferentiatingbetweenbenignandmalignantthoraciclymphnodesametaanalysis AT yingfengwu artificialintelligenceassistedendobronchialultrasoundfordifferentiatingbetweenbenignandmalignantthoraciclymphnodesametaanalysis AT linawang artificialintelligenceassistedendobronchialultrasoundfordifferentiatingbetweenbenignandmalignantthoraciclymphnodesametaanalysis AT lipinglyu artificialintelligenceassistedendobronchialultrasoundfordifferentiatingbetweenbenignandmalignantthoraciclymphnodesametaanalysis AT xiaomeilyu artificialintelligenceassistedendobronchialultrasoundfordifferentiatingbetweenbenignandmalignantthoraciclymphnodesametaanalysis |