2022 Volume 40 Issue 5 Pages 207-217
In Japan, mammography (MMG) alone or in combination with palpation is used for primary breast cancer screening. In MMG imaging, it is difficult to distinguish breast cancer in “dense breast” with many mammary glands and little fat, or in subjects in their 30s or younger, who have well-developed mammary gland, because the white areas in the images are more likely to appear white. This is particularly true in the case of breast masses, which have lower sensitivity and specificity. If the accuracy of classifying benign and malignant shadows of breast cancer masses in MMG-imaging images could be improved, it would lead to earlier detection of breast cancer. In this study, we developed an image quality improvement process that simultaneously improves the granularity and contrast of images to enhance the performance of CAD for benign and malignant classification using MMG images. As a result, the accuracy was improved to 89% when the image quality improvement process was applied, compared to 85% when the MMG images without image quality improvement process were used for benign-malignant classification. We also analyzed the factors that the learned AI focused on in the image when making discrimination in the CAD process for benign-malignant classification and report the results. In general, the screening by using screening MMG alone or in combination with palpation, and differential diagnosis is made by using diagnostic MMG, ultrasonography, and MRI at the second stage of detailed examination. This study proposes that improved image quality will enable differentiation even with screening MMG and increase the possibility of early detection of breast cancer.
