A prediction model for underestimation of invasive breast cancer after a biopsy diagnosis of ductal carcinoma in situ: based on 2 892 biopsies and 589 invasive cancers

Claudia J.C. Meurs, Joost van Rosmalen, Marian B.E. Menke-Pluijmers, Albert P.M. ter Braak, Linda de Munck, Sabine Siesling, Pieter J. Westenend (Corresponding Author)

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Abstract

Background: Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) might be diagnosed with invasive breast cancer at excision, a phenomenon known as underestimation. Patients with DCIS are treated based on the risk of underestimation or progression to invasive cancer. The aim of our study was to expand the knowledge on underestimation and to develop a prediction model. Methods: Population-based data were retrieved from the Dutch Pathology Registry and the Netherlands Cancer Registry for DCIS between January 2011 and June 2012. Results: Of 2892 DCIS biopsies, 21% were underestimated invasive breast cancers. In multivariable analysis, risk factors were high-grade DCIS (odds ratio (OR) 1.43, 95% confidence interval (CI): 1.05–1.95), a palpable tumour (OR 2.22, 95% CI: 1.76–2.81), a BI-RADS (Breast Imaging Reporting and Data System) score 5 (OR 2.36, 95% CI: 1.80–3.09) and a suspected invasive component at biopsy (OR 3.84, 95% CI: 2.69–5.46). The predicted risk for underestimation ranged from 9.5 to 80.2%, with a median of 14.7%. Of the 596 invasive cancers, 39% had unfavourable features. Conclusions: The risk for an underestimated diagnosis of invasive breast cancer after a biopsy diagnosis of DCIS is considerable. With our prediction model, the individual risk of underestimation can be calculated based on routinely available preoperatively known risk factors (https://www.evidencio.com/models/show/1074).

Original languageEnglish
Pages (from-to)1155-1162
Number of pages8
JournalBritish journal of cancer
Volume119
Issue number9
Early online date17 Oct 2018
DOIs
Publication statusPublished - 30 Oct 2018

Keywords

  • DCIS
  • Ductal carcinoma in situ
  • Breast cancer
  • Underestimation
  • Upstaging
  • Risk factors
  • Prediction model
  • Unfavourable features
  • Minimal-volume DCIS

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