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Proceedings Paper

Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?
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Paper Abstract

Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic features to predict upstaging of DCIS. The goal was to provide intentionally conservative baseline performance using readily available data from radiologists and pathologists and only linear models. We conducted a retrospective analysis on 99 patients with DCIS. Of those 25 were upstaged to invasive cancer at the time of definitive surgery. Pre-operative factors including both the histologic features extracted from stereotactic core needle biopsy (SCNB) reports and the mammographic features annotated by an expert breast radiologist were investigated with statistical analysis. Furthermore, we built classification models based on those features in an attempt to predict the presence of an occult invasive component in DCIS, with generalization performance assessed by receiver operating characteristic (ROC) curve analysis. Histologic features including nuclear grade and DCIS subtype did not show statistically significant differences between cases with pure DCIS and with DCIS plus invasive disease. However, three mammographic features, i.e., the major axis length of DCIS lesion, the BI-RADS level of suspicion, and radiologist’s assessment did achieve the statistical significance. Using those three statistically significant features as input, a linear discriminant model was able to distinguish patients with DCIS plus invasive disease from those with pure DCIS, with AUC-ROC equal to 0.62. Overall, mammograms used for breast screening contain useful information that can be perceived by radiologists and help predict occult invasive components in DCIS.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342X (3 March 2017); doi: 10.1117/12.2255847
Show Author Affiliations
Bibo Shi, Duke Univ. School of Medicine (United States)
Lars J. Grimm, Duke Univ. School of Medicine (United States)
Maciej A. Mazurowski, Duke Univ. School of Medicine (United States)
Jeffrey R. Marks, Duke Univ. School of Medicine (United States)
Lorraine M. King, Duke Univ. School of Medicine (United States)
Carlo C. Maley, Arizona State Univ. (United States)
E. Shelley Hwang, Duke Univ. School of Medicine (United States)
Joseph Y. Lo, Duke Univ. School of Medicine (United States)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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