Journal Article

Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis

Chui, Man-Lik, Cai, Jing, Ying, Michael Tin-Cheung, Han, Xinyang, Qin, Jing, Chu, Winnie Chiu-Wing, King, Ann Dorothy, Chen, Ziman, Gunda, Simon Takadiyi, Qu, Jingguo

2025

Overview

Background and objectives Accurate 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. Methods The 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. Results A 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. Conclusion AI 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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Analysis / Artificial Intelligence / Benign / Biomedical and Life Sciences / Biomedicine / Biopsy / Cancer / Cancer Research / Care and treatment / Citation management software / Classification / Computer-aided diagnosis / Development and progression / Diagnosis / Head and neck cancer / Health aspects / Health Promotion and Disease Prevention / Humans / Invasiveness / Learning algorithms / Lymph node / Lymph nodes / Lymph Nodes - diagnostic imaging / Lymph Nodes - pathology / Lymphadenopathy / Lymphadenopathy - diagnosis / Lymphadenopathy - diagnostic imaging / Lymphadenopathy - pathology / Lymphatic Metastasis / Lymphatic system / Machine learning / Medicine/Public Health / Meta-analysis / Metastasis / Neural networks / Oncology / Patients / Probability learning / Prognosis / Radiomics / Sensitivity analysis / Sensitivity and Specificity / Surgical Oncology / Systematic Review / Ultrasonic imaging / Ultrasonography / Ultrasonography - methods / Ultrasound / Ultrasound imaging