Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis
Abstract 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...
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2025-01-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-13447-y |
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| author | Xinyang Han Jingguo Qu Man-Lik Chui Simon Takadiyi Gunda Ziman Chen Jing Qin Ann Dorothy King Winnie Chiu-Wing Chu Jing Cai Michael Tin-Cheung Ying |
| author_facet | Xinyang Han Jingguo Qu Man-Lik Chui Simon Takadiyi Gunda Ziman Chen Jing Qin Ann Dorothy King Winnie Chiu-Wing Chu Jing Cai Michael Tin-Cheung Ying |
| author_sort | Xinyang Han |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-13eb83a4a8944b74abfa2e9d4f0d3912 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-13eb83a4a8944b74abfa2e9d4f0d39122025-01-19T12:27:02ZengBMCBMC Cancer1471-24072025-01-0125111410.1186/s12885-025-13447-yArtificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysisXinyang Han0Jingguo Qu1Man-Lik Chui2Simon Takadiyi Gunda3Ziman Chen4Jing Qin5Ann Dorothy King6Winnie Chiu-Wing Chu7Jing Cai8Michael Tin-Cheung Ying9The Department of Health Technology and Informatics, The Hong Kong Polytechnic UniversityThe Department of Health Technology and Informatics, The Hong Kong Polytechnic UniversityThe Department of Health Technology and Informatics, The Hong Kong Polytechnic UniversityThe Department of Health Technology and Informatics, The Hong Kong Polytechnic UniversityThe Department of Health Technology and Informatics, The Hong Kong Polytechnic UniversityCentre for Smart Health and School of Nursing, The Hong Kong Polytechnic UniversityDepartment of Imaging and Interventional Radiology, The Chinese University of Hong KongDepartment of Imaging and Interventional Radiology, The Chinese University of Hong KongThe Department of Health Technology and Informatics, The Hong Kong Polytechnic UniversityThe Department of Health Technology and Informatics, The Hong Kong Polytechnic UniversityAbstract 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.https://doi.org/10.1186/s12885-025-13447-yUltrasonographyLymph nodeMachine learningRadiomicsComputer-aided diagnosis |
| spellingShingle | Xinyang Han Jingguo Qu Man-Lik Chui Simon Takadiyi Gunda Ziman Chen Jing Qin Ann Dorothy King Winnie Chiu-Wing Chu Jing Cai Michael Tin-Cheung Ying Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis BMC Cancer Ultrasonography Lymph node Machine learning Radiomics Computer-aided diagnosis |
| title | Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis |
| title_full | Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis |
| title_fullStr | Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis |
| title_full_unstemmed | Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis |
| title_short | Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis |
| title_sort | artificial intelligence performance in ultrasound based lymph node diagnosis a systematic review and meta analysis |
| topic | Ultrasonography Lymph node Machine learning Radiomics Computer-aided diagnosis |
| url | https://doi.org/10.1186/s12885-025-13447-y |
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