Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features

Joseph Y. Lo; Jeffrey Kim; Jay A. Baker; Carey E. Floyd Jr.

doi:10.1117/12.237977

16 April 1996 Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features

Joseph Y. Lo, Jeffrey Kim, Jay A. Baker, Carey E. Floyd Jr.

Author Affiliations +

Proceedings Volume 2710, Medical Imaging 1996: Image Processing; (1996) https://doi.org/10.1117/12.237977
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

Abstract

The study aim is to develop an artificial neural network (ANN) for computer-aided diagnosis of mammography. Using 9 mammographic image features and patient age, the ANN predicted whether breast lesions were benign, invasive malignant, or noninvasive malignant. Given only 97 malignant patients, the 3-layer backpropagation ANN successfully predicted the invasiveness of those breast cancers, performing with Az of 0.88 plus or minus 0.03. To determine more generalized clinical performance, a different ANN was developed using 266 consecutive patients (97 malignant, 169 benign). This ANN predicted whether those patients were benign or noninvasive malignant vs. invasive malignant with Az of 0.86 plus or minus 0.03. This study is unique because it is the first to predict the invasiveness of breast cancers using mammographic features and age. This knowledge, which was previously available only through surgical biopsy, may assist in the planning of surgical procedures for patients with breast lesions, and may help reduce the cost and morbidity associated with unnecessary surgical biopsies.

Citation Download Citation

Joseph Y. Lo, Jeffrey Kim, Jay A. Baker, and Carey E. Floyd Jr. "Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237977

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