ROC study: effects of computer-aided diagnosis on radiologists' characterization of malignant and benign breast masses in temporal pairs of mammograms

Lubomir M. Hadjiiski; Heang-Ping Chan; Berkman Sahiner; Mark A. Helvie M.D.; Marilyn A. Roubidoux; Caroline E. Blane; Chintana Paramagul; Nicholas Petrick; Janet E. Bailey; Katherine Klein; Michelle Foster; Stephanie Patterson; Dorit D. Adler; Alexis Nees; Joseph Shen

doi:10.1117/12.483549

15 May 2003 ROC study: effects of computer-aided diagnosis on radiologists' characterization of malignant and benign breast masses in temporal pairs of mammograms

Lubomir M. Hadjiiski, Heang-Ping Chan, Berkman Sahiner, Mark A. Helvie M.D., Marilyn A. Roubidoux, Caroline E. Blane, Chintana Paramagul, Nicholas Petrick, Janet E. Bailey, Katherine Klein, Michelle Foster, Stephanie Patterson, Dorit D. Adler, Alexis Nees, Joseph Shen

Author Affiliations +

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.483549
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

We conducted an observer performance study using receiver operating characteristic (ROC) methodology to evaluate the effects of computer-aided diagnosis (CAD) on radiologists’ performance for characterization of masses on serial mammograms. The automated CAD system, previously developed in our laboratory, can classify masses as malignant or benign based on interval change information on serial mammograms. In this study, 126 temporal image pairs (73 malignant and 53 benign) from 52 patients containing masses on serial mammograms were used. The corresponding masses on each temporal pair were identified by an experienced radiologist and automatically segmented by the CAD program. Morphological, texture, and spiculation features of the mass on the current and the prior mammograms were extracted. The individual features and the difference between the corresponding current and prior features formed a multidimensional feature space. A subset of the most effective features that contained the current, prior, and interval change information was selected by a stepwise procedure and used as input predictor variables to a linear discriminant classifier in a leave-one-case-out training and testing resampling scheme. The linear discriminant classifier estimated the relative likelihood of malignancy of each mass. The classifier achieved a test Az value of 0.87. For the ROC study, 4 MQSA radiologists and 1 breast imaging fellow assessed the masses on the temporal pairs and provided estimates of the likelihood of malignancy without and with CAD. The average A_z value for the likelihood of malignancy estimated by the radiologists was 0.79 without CAD and improved to 0.87 with CAD. The improvement was statistically significant (p=0.0003). This preliminary result indicated that CAD using interval change analysis can significantly improve radiologists’ accuracy in classification of masses and thereby may increase the positive predictive value of mammography.

Citation Download Citation

Lubomir M. Hadjiiski, Heang-Ping Chan, Berkman Sahiner, Mark A. Helvie M.D., Marilyn A. Roubidoux, Caroline E. Blane, Chintana Paramagul, Nicholas Petrick, Janet E. Bailey, Katherine Klein, Michelle Foster, Stephanie Patterson, Dorit D. Adler, Alexis Nees, and Joseph Shen "ROC study: effects of computer-aided diagnosis on radiologists' characterization of malignant and benign breast masses in temporal pairs of mammograms", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.483549

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available