Share Email Print

Proceedings Paper

A computer-aided diagnosis system for prediction of the probability of malignancy of breast masses on ultrasound images
Author(s): Jing Cui; Berkman Sahiner; Heang-ping Chan; Jiazheng Shi; Alexis Nees; Chintana Paramagul; Lubomir M. Hadjiiski
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A computer-aided diagnosis (CADx) system with the ability to predict the probability of malignancy (PM) of a mass can potentially assist radiologists in making correct diagnostic decisions. In this study, we designed a CADx system using logistic regression (LR) as the feature classifier which could estimate the PM of a mass. Our data set included 488 ultrasound (US) images from 250 biopsy-proven breast masses (100 malignant and 150 benign). The data set was divided into two subsets T1 and T2. Two experienced radiologists, R1 and R2, independently provided Breast Imaging Reporting and Data System (BI-RADS) assessments and PM ratings for data subsets T2 and T1, respectively. An LR classifier was designed to estimate the PM of a mass using two-fold cross validation, in which the data subsets T1 and T2 served once as the training and once as the test set. To evaluate the performance of the system, we compared the PM estimated by the CADx system with radiologists' PM ratings (12-point scale) and BI-RADS assessments (6-point scale). The correlation coefficients between the PM ratings estimated by the radiologists and by the CADx system were 0.71 and 0.72 for data subsets T1 and T2, respectively. For the BI-RADS assessments provided by the radiologists and estimated by the CADx system, the correlation coefficients were 0.60 and 0.67 for data subsets T1 and T2, respectively. Our results indicate that the CADx system may be able to provide not only a malignancy score, but also a more quantitative estimate for the PM of a breast mass.

Paper Details

Date Published: 3 March 2009
PDF: 7 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600L (3 March 2009); doi: 10.1117/12.813722
Show Author Affiliations
Jing Cui, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Heang-ping Chan, Univ. of Michigan (United States)
Jiazheng Shi, Univ. of Michigan (United States)
Alexis Nees, Univ. of Michigan (United States)
Chintana Paramagul, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?