Associations between mammographic phenotypes and histopathologic features in ductal carcinoma in situ

Ruvini Navaratna, Aimilia Gastounioti, Meng Kang Hsieh, Lauren Pantalone, Marie Shelanski, Emily F. Conant, Despina Kontos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


With the advent of regular breast screening, ductal carcinoma in situ (DCIS) diagnoses have risen in number, now making up almost 20% of all detected breast cancers at screening. Women diagnosed with DCIS are almost universally treated. However, recent studies suggest that up to 70% of DCIS lesions will never become life-threatening, which emphasizes the need for better risk stratification strategies. Considering that histopathologic features have been shown to be predictive of DCIS aggressiveness, our aim was to study associations between DCIS histopathologic features and mammographic phenotypes towards identifying readily-available mammography-based prognostic biomarkers. To this end, breast density and parenchymal texture features were extracted from screening digital mammograms and principal component analysis was used to capture the dominant textural components. Primary analyses included statistical tests to compare feature distributions between histopathologic subgroups. Logistic regression models were, then, applied to evaluate trends in DCIS histopathologic characteristics among mammographic features, after adjustment for risk factors known to affect mammographic phenotypes. We found that HER2 had a significant association with breast percent density (p = 0.006) and the first principal component (PC1) of texture features (p = 0.034). Our risk-factor-adjusted logistic regression analyses showed that breast percent density was predictive of HER2 status (AUC = 0.71), while prediction performance was further increased when PC1 was added to the model (AUC = 0.74). These findings provide preliminary evidence about the potential value of mammographic phenotypes in prediction of DCIS aggressiveness and could ultimately contribute to identifying patients who do not require treatment.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
ISBN (Electronic)9781510625471
StatePublished - 2019
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: Feb 17 2019Feb 20 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego


  • Breast cancer risk
  • DCIS
  • Digital mammography
  • Ductal carcinoma in situ
  • Histopathology


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