Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. When cancer is found, the best treatment method is selected considering the cancer subtypes. In this study, we investigated a method to distinguish breast cancers with poor prognosis from those with relatively good prognosis to assist diagnosis and treatment planning. In our previous study, all regions of interest including cancer lesions were resized to the same matrix size, which had caused loss of size and local characteristic information of the lesions. In this study, local patches with the original pixel size were automatically selected during the training in each epoch. The patch sampling could also reduce the effect of class imbalance. The proposed model was tested using 264 cases by a 4-fold cross validation. The result indicates the potential usefulness of the proposed method. The computerized subtype classification may support a prompt treatment planning and proper patient care.
Success of breast cancer treatment is subject to various factors, including cancer stage and cancer grade. The best treatment is selected based on the characteristic of cancer. It is desirable to predict the cancer characteristics and prognostic factors accurately and promptly by diagnostic imaging. The purpose of the study is to investigate the use of multimodality diagnostic images in predicting breast cancer subtypes to assist diagnosis and treatment planning. In this study, we classify lesions into molecular subtypes and simultaneously predict histological grades and invasiveness of the cancers by mammography and breast ultrasound images. Models with different architectures including single input and multi-input layers with single head and multiple head models are compared. The results indicate that use of multimodality images is more predictive than using single modalities. The automatic subtype classification using multimodality images may support a prompt treatment planning and proper patient care.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.