Diagnostic performance of DCE-MRI radiomics in predicting axillary lymph node metastasis in breast cancer patients: A meta-analysis

Fei Dong, Jie Li, Junbo Wang and Xiaohui Yang

PLOS ONE, 2024, vol. 19, issue 12, 1-17

Abstract: Radiomics offers a novel strategy for the differential diagnosis, prognosis evaluation, and prediction of treatment responses in breast cancer. Studies have explored radiomic signatures from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM), but the diagnostic accuracy varies widely. To evaluate this performance, we conducted a meta-analysis performing a comprehensive literature search across databases including PubMed, EMBASE, SCOPUS, Web of Science (WOS), Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, and the Chinese BioMedical Literature Database (CBM) until March 31, 2024. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC) were calculated. Twenty-four eligible studies encompassing 5588 breast cancer patients were included in the meta-analysis. The meta-analysis yielded a pooled sensitivity of 0.81 (95% confidence interval [CI]: 0.77–0.84), specificity of 0.85 (95%CI: 0.81–0.87), PLR of 5.24 (95%CI: 4.32–6.34), NLR of 0.23 (95%CI: 0.19–0.27), DOR of 23.16 (95%CI: 17.20–31.19), and AUC of 0.90 (95%CI: 0.87–0.92), indicating good diagnostic performance. Significant heterogeneity was observed in analyses of sensitivity (I2 = 74.64%) and specificity (I2 = 83.18%). Spearman’s correlation coefficient suggested no significant threshold effect (P = 0.538). Meta-regression and subgroup analyses identified several potential heterogeneity sources, including data source, integration of clinical factors and peritumor features, MRI equipment, magnetic field strength, lesion segmentation, and modeling methods. In conclusion, DCE-MRI radiomic models exhibit good diagnostic performance in predicting ALNM and SLNM in breast cancer. This non-invasive and effective tool holds potential for the preoperative diagnosis of lymph node metastasis in breast cancer patients.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0314653

DOI: 10.1371/journal.pone.0314653

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