SynthesisBMC cancer2025
Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis.
Synthesis in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis.BMC pulmonary medicine · 2025Pooled it
- ViT-radiomics fusion for lymph node classification in ultrasound images: a multicenter study.La Radiologia medica · 2026Article
- Fusion model combining ultrasound-based radiomics and deep transfer learning with clinical parameters for preoperative prediction of pelvic lymph node metastasis in cervical cancer.Frontiers in oncology · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND AND
objectivesAccurate classification of lymphadenopathy is essential for determining the pathological nature of lymph nodes (LNs), which plays a crucial role in treatment selection. The biopsy method is invasive and carries the risk of sampling failure, while the utilization of non-invasive approaches such as ultrasound can minimize the probability of iatrogenic injury and infection. With the advancement of artificial intelligence (AI) and machine learning, the diagnostic efficiency of LNs is further enhanced. This study evaluates the performance of ultrasound-based AI applications in the classification of benign and malignant LNs.
methodsThe literature research was conducted using the PubMed, EMBASE, and Cochrane Library databases as of June 2024. The quality of the included studies was evaluated using the QUADAS-2 tool. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated to assess the diagnostic efficacy of ultrasound-based AI in classifying benign and malignant LNs. Subgroup analyses were also conducted to identify potential sources of heterogeneity.
resultsA total of 1,355 studies were identified and reviewed. Among these studies, 19 studies met the inclusion criteria, and 2,354 cases were included in the analysis. The pooled sensitivity, specificity, and DOR of ultrasound-based machine learning in classifying benign and malignant LNs were 0.836 (95% CI [0.805, 0.863]), 0.850 (95% CI [0.805, 0.886]), and 33.331 (95% CI [22.873, 48.57]), respectively, indicating no publication bias (p = 0.12). Subgroup analyses may suggest that the location of lymph nodes, validation methods, and type of primary tumor are the sources of heterogeneity.
conclusionAI can accurately differentiate benign from malignant LNs. Given the widespread use of ultrasonography in diagnosing malignant LNs in cancer patients, there is significant potential for integrating AI-based decision support systems into clinical practice to enhance the diagnostic accuracy.
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