A preoperative nomogram to predict the risk of synchronous distant metastases at diagnosis of primary breast cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-03

AUTHORS

C Boutros, C Mazouni, F Lerebours, D Stevens, X Lei, A M Gonzalez-Angulo, S Delaloge

ABSTRACT

BACKGROUND: The detection of synchronous metastases at primary diagnosis of breast cancer (BC) affects its initial management. A risk calculator that incorporates many factors to evaluate an individual's risk of harbouring synchronous metastases would be useful to adapt cancer management. PATIENTS AND METHODS: Patients with primary diagnosis of BC were identified from three institutional databases sharing homogeneous work-up recommendations. A risk score for synchronous metastases was estimated and a nomogram was constructed using the first database. Its performance was assessed by receiver characteristic (ROC) analysis. The nomogram was externally validated in the two independent cohorts. RESULTS: A preoperative nomogram based on the clinical tumour size (P<0.001), clinical nodal status (P<0.001), oestrogen (P=0.17) and progesterone receptors (P=0.04) was developed. The nomogram accuracy was 87.3% (95% confidence interval (CI), 84.45-90.2%). Overall, the area under the ROC curve (AUC) was 86.1% for the validation set from the Institut Curie-René Huguenin, and 63.8% for the MD Anderson validation set. The negative predictive value (NPV) was high in the three cohorts (97-99%). CONCLUSIONS: We developed and validated a strong metastasis risk calculator that can evaluate with high accuracy an individual's risk of harbouring synchronous metastases at diagnosis of primary BC. CONDENSED ABSTRACT: A nomogram to predict synchronous metastases at diagnosis of breast cancer was developed and externally validated. This tool allows avoiding unnecessary expensive work-up. More... »

PAGES

992

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/bjc.2015.34

DOI

http://dx.doi.org/10.1038/bjc.2015.34

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1006331361

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25668007


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