1. Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis
- Author
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Shiju Yan, Yunzhi Wang, Faranak Aghaei, Yuchen Qiu, and Bin Zheng
- Subjects
Fusion image ,Artificial neural network ,Receiver operating characteristic ,Computer science ,business.industry ,Pattern recognition ,Optical density ,computer.software_genre ,medicine.disease ,Grayscale ,030218 nuclear medicine & medical imaging ,Image conversion ,Image (mathematics) ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,030220 oncology & carcinogenesis ,medicine ,Radiology, Nuclear Medicine and imaging ,Data mining ,Artificial intelligence ,business ,computer - Abstract
Rationale and Objectives The purpose of this study is to improve accuracy of near-term breast cancer risk prediction by applying a new mammographic image conversion method combined with a two-stage artificial neural network (ANN)-based classification scheme. Materials and Methods The dataset included 168 negative mammography screening cases. In developing and testing our new risk model, we first converted the original grayscale value (GV)-based mammographic images into optical density (OD)-based images. For each case, our computer-aided scheme then computed two types of image features representing bilateral asymmetry and the maximum of the image features computed from GV and OD images, respectively. A two-stage classification scheme consisting of three ANNs was developed. The first stage included two ANNs trained using features computed separately from GV and OD images of 138 cases. The second stage included another ANN to fuse the prediction scores produced by two ANNs in the first stage. The risk prediction performance was tested using the rest 30 cases. Results With the two-stage classification scheme, the computed area under the receiver operating characteristic curve (AUC) was 0.816 ± 0.071, which was significantly higher than the AUC values of 0.669 ± 0.099 and 0.646 ± 0.099 achieved using two ANNs trained using GV features and OD features, respectively ( P Conclusion This study demonstrated that applying an OD image conversion method can acquire new complimentary information to those acquired from the original images. As a result, fusion image features computed from these two types of images yielded significantly higher performance in near-term breast cancer risk prediction.
- Published
- 2022
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