IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0314653.html
   My bibliography  Save this article

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

Author

Listed:
  • Fei Dong
  • Jie Li
  • Junbo Wang
  • Xiaohui Yang

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.

Suggested Citation

  • Fei Dong & Jie Li & Junbo Wang & Xiaohui Yang, 2024. "Diagnostic performance of DCE-MRI radiomics in predicting axillary lymph node metastasis in breast cancer patients: A meta-analysis," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0314653
    DOI: 10.1371/journal.pone.0314653
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0314653
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0314653&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0314653?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Daniel J. Luckett & Eric B. Laber & Samer S. El‐Kamary & Cheng Fan & Ravi Jhaveri & Charles M. Perou & Fatma M. Shebl & Michael R. Kosorok, 2021. "Receiver operating characteristic curves and confidence bands for support vector machines," Biometrics, The International Biometric Society, vol. 77(4), pages 1422-1430, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Riikka Numminen & Ileana Montoya Perez & Ivan Jambor & Tapio Pahikkala & Antti Airola, 2023. "Quicksort leave-pair-out cross-validation for ROC curve analysis," Computational Statistics, Springer, vol. 38(3), pages 1579-1595, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0314653. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.