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Associations between mammographic phenotypes and histopathologic features in ductal carcinoma in situ
Author(s): Ruvini Navaratna; Aimilia Gastounioti; Meng-Kang Hsieh; Lauren Pantalone; Marie Shelanski; Emily F. Conant; Despina Kontos
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Paper Abstract

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.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502K (13 March 2019); doi: 10.1117/12.2512464
Show Author Affiliations
Ruvini Navaratna, Univ. of Pennsylvania (United States)
Aimilia Gastounioti, Univ. of Pennsylvania (United States)
Meng-Kang Hsieh, Univ. of Pennsylvania (United States)
Lauren Pantalone, Univ. of Pennsylvania (United States)
Marie Shelanski, Univ. of Pennsylvania (United States)
Emily F. Conant, Univ. of Pennsylvania (United States)
Despina Kontos, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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