Incorporation of clinical data into a computerized method for the assessment of mammographic breast lesions

Zhimin Huo; Maryellen Lissak Giger

doi:10.1117/12.387626

6 June 2000 Incorporation of clinical data into a computerized method for the assessment of mammographic breast lesions

Zhimin Huo, Maryellen Lissak Giger

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387626
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

We previously developed a computerized method to classify mammographic masses as benign or malignant. In this method, mammographic features that are similar to the ones used by radiologists are automatically extracted to characterize a mass lesion. These features are then merged by an artificial neural network (ANN), which yields an estimated likelihood of malignancy for each mass. The performance of the method was evaluated on an independent database consisting of 110 cases (60 benign and 50 malignant cases). The method achieved an A_z of 0.91 from round-robin analysis in the task of differentiating between benign and malignant masses using the computer-extracted features only. As the most important clinical risk factor for breast cancer, age achieved a performance level (A_z equals 0.79) similar to that (A_z equals 0.77 and 0.80) of the computer-extracted spiculation features, which are the most important indicators for malignancy of a mass, in differentiating between the malignant and benign cases. In this study, age is included as an additional input feature to the ANN. The performance of the scheme (A_z equals 0.93) is improved when age is included. However, the improvement is not found to be statistically significant. Our results indicated that age may be a strong feature in predicting malignancy of a mass. For this database, however, the inclusion of age may not have a strong impact on the determination of the likelihood for a mammographic mass lesion when the major mammographic characteristics (e.g., spiculation) of a mass are accurately extracted and analyzed along with other features using an artificial neural network.

Citation Download Citation

Zhimin Huo and Maryellen Lissak Giger "Incorporation of clinical data into a computerized method for the assessment of mammographic breast lesions", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387626

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