Abstract
Background: The presence of lymph node metastases (LNMs) is one of the most important prognostic factors in breast cancer.
Purpose: To correlate a detailed catalog of 17 descriptors in breast MRI (bMRI) with the presence of LNMs and to identify useful combinations of such descriptors for the prediction of LNMs using a dedicated decision tree.
Material and Methods: A standardized protocol and study design was applied in this IRB-approved study (T1-weighted FLASH; 0.1 mmol/kg body weight Gd-DTPA; T2-weighted TSE; histological verification after bMRI). Two experienced radiologists performed prospective evaluation of the previously acquired examination in consensus. In every lesion 17 previously published descriptors were assessed. Subgroups of primary breast cancers with (N+: 97) and without LNM were created (N−: 253). The prevalence and diagnostic accuracy of each descriptor were correlated with the presence of LNM (chi-square test; diagnostic odds ratio/DOR). To identify useful combinations of descriptors for the prediction of LNM a chi-squared automatic interaction detection (CHAID) decision tree was applied.
Results: Seven of 17 descriptors were significantly associated with LNMs. The most accurate were “Skin thickening” (P < 0.001; DOR 5.9) and “Internal enhancement” (P < 0.001; DOR ≤13.7). The CHAID decision tree identified useful combinations of descriptors: “Skin thickening” plus “Destruction of nipple line” raised the probability of N+ by 40% (P< 0.05). In case of absence of “Skin thickening”, “Edema”, and “Irregular margins”, the likelihood of N+ was 0% (P<0.05).
Conclusion: Our data demonstrate the close association of selected breast MRI descriptors with nodal status. If present, such descriptors can be used – as stand alone or in combination – to accurately predict LNM and to stratify the patient's prognosis.
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Acknowledgments
Generation of the long-term database for this study has been made possible by ongoing contributions from the whole team over many years. Especially, we would like to thank Mrs Heike Habrecht, who contributed to this work during her research for her thesis. Furthermore, we'd like to thank Dipl. math. Antje Brandstädt from the Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich-Schiller Universität Jena for her precious help in statistical analysis.
Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.