Journal of Clinical Oncology · 2010 · 71 citations · 22 references
MedicineBiostatisticsOncologyBreast OncologyClinical Decision SupportSurvival AnalysisBreast Cancer RecurrenceBreast SurgeryCancer Survival AnalysisRisk PredictionSurvival ModelingSurvival PredictionBreast Cancer PrognosisCancer Prognostic ModelingCancer Risk Prediction ModelsClinical Risk Prediction ToolsClinicopathologic FeaturesCox RegressionPredictive NomogramsRecurrence After Breast-conserving Surgery
PURPOSE IBTR! version 1.0 is a web-based tool that uses literature-derived relative risk ratios for seven clinicopathologic variables to predict ipsilateral breast tumor recurrence (IBTR) after breast-conserving therapy (BCT). Preliminary testing demonstrated over-estimation in high-risk subgroups. This study uses two independent population-based datasets to create and validate a modified nomogram, IBTR! version 2.0. METHODS Cox regression modeling was performed on 7,811 patients treated with BCT at the British Columbia Cancer Agency (median follow-up, 9.4 years). Population-based hazard ratios were generated for the seven variables in the original nomogram. A modified nomogram was then tested against 664 patients from Massachusetts General Hospital (median follow-up, 9.3 years). The mean predicted and observed 10-year estimates were compared for the entire cohort and for four groups predefined by nomogram-predicted risks: group 1: less than 3%; group 2: 3% to 5%; group 3: 5% to 10%; and group 4: more than 10%. Results IBTR! version 2.0 predicted an overall 10-year IBTR estimate of 4.0% (95% CI, 3.8 to 4.2), while the observed estimate was 2.8% (95% CI, 1.6 to 4.7; P = .10). The predicted and observed IBTR estimates were: group 1 (n = 283): 2.2% versus 1.3%, P = .40; group 2 (n = 237): 3.8% versus 3.5%, P = .80; group 3 (n = 111): 6.7% versus 3.2%, P = .05; and group 4 (n = 33): 12.5% versus 8.7%, P = .50. CONCLUSION IBTR! version 2.0 is accurate in the majority of patients with a low to moderate risk of in-breast recurrence. The nomogram still overestimates risk in a minority of patients with higher risk features. Validation in a larger prospective data set is warranted.
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