Mixed-methods study to predict upstaging of DCIS to invasive disease on mammography.

Published online

Journal Article

Background: The incidence of DCIS has steadily increased with growing concerns regarding overtreatment. Active surveillance is a novel treatment strategy that avoids surgical excision, but identifying patients with occult invasive disease who should be excluded from active surveillance is challenging. Predicting upstaging of DCIS to invasive disease is not a current task for radiologists, but radiologist might be trained to improve their predictive performance. Objective: To determine if a mixed-methods two-stage observer study can improve radiologists' ability to predict upstaging of DCIS to invasive disease on mammography. Methods: All cases of stereotactically biopsied DCIS calcifications between 2010 and 2015 were identified. Two cohorts were randomly generated, each containing 150 cases: 120 pure DCIS cases and 30 DCIS cases upstaged to invasive disease at surgery. Nine breast radiologists reviewed the mammograms in the first cohort in a blinded fashion and scored the probability of upstaging to invasive disease. The radiologists then reviewed the cases and results collectively in a focus group to develop consensus criteria that could improve their performance. The radiologists reviewed the mammograms from the second cohort in a blinded fashion and again scored the probability of upstaging. Statistical analysis compared the performance between rounds 1 and 2. Results: Mean reader performance to predict upstaging in round 1 was an AUC of 0.623 (range: 0.541-0.684). In the focus group, radiologists agreed that 1) an associated mass, asymmetry, or architectural distortion, 2) larger extent of calcifications if densely packed, and 3) focusing on the most suspicious rather than most common features, better predicted upstaging. There was agreement that BI-RADS descriptors do not adequately characterize risk of invasion and that microinvasive disease and smaller areas of DCIS will have poor prediction estimates. The mean reader performance significantly improved in round 2 (mean AUC: 0.765; AUC range: 0.617-0.852; p=0.045). Conclusions: A mixed-methods two-stage observer study identified factors that helped radiologist significantly improve their ability to predict upstaging of DCIS to invasive disease. Clinical impact: Breast radiologists can be trained to improve their ability to predict upstaging of DCIS to invasive disease which may facilitate discussions with patients and referring providers.

Full Text

Duke Authors

Cited Authors

  • Grimm, LJ; Neely, B; Hou, R; Selvakumaran, V; Baker, JA; Yoon, SC; Ghate, SV; Walsh, R; Litton, TP; Devalapalli, A; Kim, C; Soo, MS; Hyslop, T; Hwang, ES; Lo, JY

Published Date

  • August 12, 2020

Published In

PubMed ID

  • 32783550

Pubmed Central ID

  • 32783550

Electronic International Standard Serial Number (EISSN)

  • 1546-3141

Digital Object Identifier (DOI)

  • 10.2214/AJR.20.23679


  • eng

Conference Location

  • United States