AUTHOR=Li Jun , Wang Si-Rui , Li Qiao-Li , Zhu Tong , Zhu Pei-Shan , Chen Ming , Cui Xin-Wu TITLE=Diagnostic value of multiple ultrasound diagnostic techniques for axillary lymph node metastases in breast cancer: A systematic analysis and network meta-analysis JOURNAL=Frontiers in Oncology VOLUME=12 YEAR=2023 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.1043185 DOI=10.3389/fonc.2022.1043185 ISSN=2234-943X ABSTRACT=Background

Early diagnosis of axillary lymph node metastasis is very important for the recurrence and prognosis of breast cancer. Currently, Lymph node biopsy is one of the important methods to detect lymph node metastasis in breast cancer, however, its invasiveness might bring complications to patients. Therefore, this study investigated the diagnostic performance of multiple ultrasound diagnostic methods for axillary lymph node metastasis of breast cancer.

Materials and methods

In this study, we searched PubMed, Web of Science, CNKI and Wan Fang databases, conducted Bayesian network meta-analysis (NMA) on the studies that met the inclusion criteria, and evaluated the consistency of five different ultrasound imaging techniques in axillary lymph node metastasis of breast cancer. Funnel graph was used to evaluate whether it had publication bias. The diagnostic performance of each ultrasound imaging method was ranked using SUCRA

Results

A total of 22 papers were included, US+CEUS showed the highest SUCRA values in terms of sensitivity (SEN) (0.874), specificity (SPE) (0.911), positive predictive value (PPV) (0.972), negative predictive value (NPV) (0.872) and accuracy (ACC) (0.990).

Conclusion

In axillary lymph node metastasis of breast cancer, the US+CEUS combined diagnostic method showed the highest SUCRA value among the five ultrasound diagnostic methods. This study provides a theoretical basis for preoperative noninvasive evaluation of axillary lymph node metastases in breast cancer patients and clinical treatment decisions.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022351977.