ArticleBreast (Edinburgh, Scotland)2023
Clinicopathologic and sociodemographic factors associated with late relapse triple negative breast cancer in a multivariable logistic model: A multi-institution cohort study.
Article in Breast (Edinburgh, Scotland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Analysis of prognostic factors after pulmonary resection for metastatic breast cancer: a 23-year single-institution retrospective study.General thoracic and cardiovascular surgery · 2026Article
- Indolent triple negative breast cancer patients are more likely to develop late relapse compared to triple negative breast cancer patients with a high proliferation index.Breast (Edinburgh, Scotland) · 2023Article
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13 authors.
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Abstract
backgroundMost metastatic recurrences of triple negative breast cancer (TNBC) occur within five years of diagnosis, yet late relapses of TNBC (lrTNBC) do occur. Our objective was to develop a risk prediction model of lrTNBC using readily available clinicopathologic and sociodemographic features.
methodsWe included patients diagnosed with stage I-III TNBC between 1998 and 2012 at ten academic cancer centers. lrTNBC was defined as relapse or mortality greater than 5 years from diagnosis. Features associated with lrTNBC were included in a multivariable logistic model using backward elimination with a p < 0.10 criterion, with a final multivariable model applied to training (70%) and independent validation (30%) cohorts.
resultsA total 2210 TNBC patients with at least five years follow-up and no relapse before 5 years were included. In final multivariable model, lrTNBC was significantly associated with higher stage at diagnosis (adjusted Odds Ratio [aOR] for stage III vs I, 10.9; 95% Confidence Interval [CI], 7.5-15.9; p < 0.0001) and BMI (aOR for obese vs normal weight, 1.4; 95% CI, 1.0-1.8; p = 0.03). Final model performance was consistent between training (70%) and validation (30%) cohorts.
conclusionsA risk prediction model incorporating stage, BMI, and age at diagnosis offers potential utility for identification of patients at risk of development of lrTNBC and warrants further investigation.
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