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Proceedings Paper

Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features
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Paper Abstract

Predicting the risk of occult invasive disease in ductal carcinoma in situ (DCIS) is an important task to help address the overdiagnosis and overtreatment problems associated with breast cancer. In this work, we investigated the feasibility of using computer-extracted mammographic features to predict occult invasive disease in patients with biopsy proven DCIS. We proposed a computer-vision algorithm based approach to extract mammographic features from magnification views of full field digital mammography (FFDM) for patients with DCIS. After an expert breast radiologist provided a region of interest (ROI) mask for the DCIS lesion, the proposed approach is able to segment individual microcalcifications (MCs), detect the boundary of the MC cluster (MCC), and extract 113 mammographic features from MCs and MCC within the ROI. In this study, we extracted mammographic features from 99 patients with DCIS (74 pure DCIS; 25 DCIS plus invasive disease). The predictive power of the mammographic features was demonstrated through binary classifications between pure DCIS and DCIS with invasive disease using linear discriminant analysis (LDA). Before classification, the minimum redundancy Maximum Relevance (mRMR) feature selection method was first applied to choose subsets of useful features. The generalization performance was assessed using Leave-One-Out Cross-Validation and Receiver Operating Characteristic (ROC) curve analysis. Using the computer-extracted mammographic features, the proposed model was able to distinguish DCIS with invasive disease from pure DCIS, with an average classification performance of AUC = 0.61 ± 0.05. Overall, the proposed computer-extracted mammographic features are promising for predicting occult invasive disease in DCIS.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341I (3 March 2017); doi: 10.1117/12.2255731
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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