Predicting Upstaging of DCIS to Invasive Disease: Radiologists's Predictive Performance.

Journal Article (Journal Article)

RATIONALE AND OBJECTIVES: The purpose of this study is to quantify breast radiologists' performance at predicting occult invasive disease when ductal carcinoma in situ (DCIS) presents as calcifications on mammography and to identify imaging and histopathological features that are associated with radiologists' performance. MATERIALS AND METHODS: Mammographically detected calcifications that were initially diagnosed as DCIS on core biopsy and underwent definitive surgical excision between 2010 and 2015 were identified. Thirty cases of suspicious calcifications upstaged to invasive ductal carcinoma and 120 cases of DCIS confirmed at the time of definitive surgery were randomly selected. Nuclear grade, estrogen and progesterone receptor status, patient age, calcification long axis length, and breast density were collected. Ten breast radiologists who were blinded to all clinical and pathology data independently reviewed all cases and estimated the likelihood that the DCIS would be upstaged to invasive disease at surgical excision. Subgroup analysis was performed based on nuclear grade, long axis length, breast density and after exclusion of microinvasive disease. RESULTS: Reader performance to predict upstaging ranged from an area under the receiver operating characteristic curve (AUC) of 0.541-0.684 with a mean AUC of 0.620 (95%CI: 0.489-0.751). Performances improved for lesions smaller than 2 cm (AUC: 0.676 vs 0.500; p = 0.002). The exclusion of microinvasive cases also improved performance (AUC: 0.651 vs 0.620; p = 0.005). There was no difference in performance based on breast density (p = 0.850) or nuclear grade (p = 0.270) CONCLUSION: Radiologists were able to predict invasive disease better than chance, particularly for smaller DCIS lesions (<2 cm) and after the exclusion of microinvasive disease.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • November 2020

Published In

Volume / Issue

  • 27 / 11

Start / End Page

  • 1580 - 1585

PubMed ID

  • 32001164

Pubmed Central ID

  • PMC7382977

Electronic International Standard Serial Number (EISSN)

  • 1878-4046

Digital Object Identifier (DOI)

  • 10.1016/j.acra.2019.12.009


  • eng

Conference Location

  • United States