Assessment of a new CAD-generated imaging marker to predict risk of having mammography-occult tumors

Abstract

Since sensitivity of mammography is limited in detecting subtle cancer, we propose to develop a new computer-aided detection (CAD) scheme to generate a new quantitative image marker to predict risk of having mammography-occult cancer that can be detected by breast MRI. The study is based on the hypothesis that overall breast density and bilateral asymmetry of breast density between left and right breasts associate with higher risk of developing breast cancer in shortterm. Thus, a new CAD scheme is developed to process images and analyze bilateral asymmetry of mammographic density and tissue structure. From the computed image features, a machine learning model is trained to generate an image marker or likelihood score to predict risk of having mammography-occult tumors. In this presentation, we report two cases in which screening mammograms are rated as BIRADS 2 by radiologists. Two women do not qualify for breast MRI screening due to the lower risk scores predicted by existing epidemiology risk models. CAD scheme analyzes mammograms of these two cases and produces high risk scores of having mammography-occult tumors. After applying breast MRI screening, two mammography-occult tumors are detected. Biopsy results confirm one invasive ductal carcinoma (grade 3) and one high-risk tumor of solitary breast papillomas, which needs to be removed by surgery. This study demonstrates potential advantages of applying CAD-generated image marker to detect abnormality or predict cancer risk that are missed or overlooked by radiologists. It can thus increase efficacy of using MRI as an adjunct tool to mammography to detect more subtle cancers.