Predictors of preoperative MRI for breast cancer: Differences by data source

Elizabeth T. Loggers, Hongyuan Gao, Laura S. Gold, Larry Kessler, Ruth Etzioni, Diana S.M. Buist

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Aim: Investigate how the results of predictive models of preoperative MRI for breast cancer change based on available data. Materials & methods: A total of 1919 insured women aged ≥18 with stage 0-III breast cancer diagnosed 2002-2009. Four models were compared using nested multivariable logistic, backwards stepwise regression; model fit was assessed via area under the curve (AUC), R. Results: MRI recipients (n = 245) were more recently diagnosed, younger, less comorbid, with higher stage disease. Significant variables included: Model 1/Claims (AUC = 0.76, R = 0.10): year, age, location, income; Model 2/Cancer Registry (AUC = 0.78, R = 0.12): stage, breast density, imaging indication; Model 3/Medical Record (AUC = 0.80, R = 0.13): radiologic recommendations; Model 4/Risk Factor Survey (AUC = 0.81, R = 0.14): procedure count. Conclusion: Clinical variables accounted for little of the observed variability compared with claims data.

Original languageEnglish (US)
Pages (from-to)215-226
Number of pages12
JournalJournal of Comparative Effectiveness Research
Issue number3
StatePublished - May 1 2015
Externally publishedYes


  • MRI
  • breast cancer
  • chart abstraction
  • claims/utilization data
  • predictive variables
  • risk factor data
  • survey data

ASJC Scopus subject areas

  • Health Policy


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