Abstract P4-02-06: Improving efficacy of applying breast MRI to detect mammography-occult breast cancer

Background: Mammography is the only clinically acceptable imaging modality in current population-based breast cancer screening, but it has a relatively low sensitivity in detecting early cancers. Although breast magnetic resonance imaging (MRI) is much more sensitive in detecting early or mammography-occult cancers, the low cancer detection yield in the general screening population (∼1%) prohibits breast MRI as a screening tool with wide applicability. In order to solve this clinical dilemma, in this preliminary study, we tested the potential of applying a new mammographic image feature analysis model that serves as a short-term breast cancer risk prediction model to identify women at high risk of harboring mammography-occult breast cancers, which can be detected by breast MRI. Methods: An image dataset involving 30 women who had both mammography and breast MRI screening examinations was retrospectively assembled. All mammograms were interpreted as negative by the radiologists during the original image interpretation. When applying breast MRI examinations to these women immediately following the negative mammography, 5 women were positive with cancer detected and 25 remained negative. We developed a computer-aided detection scheme to process the bilateral CC view mammograms of the left and right breasts. Ten (10) texture-based mammographic image features were computed and compared from the bilateral mammograms. These features were then fused to build a new artificial neural network (ANN) based risk model. Based on the threshold of 0.5 applying to the ANN-generated risk prediction scores (ranging from 0 to 1), we stratified these 30 women into two groups with high and low risk of harboring mammography-occult cancer. Results: Using the ANN-generated risk scores, 9 and 21 cases were assigned to high-risk and low-risk group, respectively. All 5 breast MRI-detected cancer cases were classified into the high-risk group (with 100% sensitivity), while 4 negative cases were also classified into the high-risk group resulting in a 16% false-positive rate (4/25). All 21 cases in the low-risk group were negative cases. Hence, our risk prediction model yielded an overall prediction accuracy of 86.7% in which 26 of 30 cases were correctly classified. The potential clinical impact is that based on the case stratification result, the maximum cancer detection yield of using breast MRI is 56% (5/9) in this dataset. Meanwhile, it can also eliminate 84% (21/25) unnecessary (negative) breast MRI screening examinations. Conclusions: This is the first study, which demonstrated that by computing and analyzing the variation of mammographic image features, we are able to develop a new quantitative image feature analysis model that can be applied to predict the short-term risk of a woman having a mammography-occult cancer that can be detected by breast MRI. This new strategy has the potential to significantly increase cancer detection yield of breast MRI and thus make breast MRI a more cost-effective imaging modality in breast cancer screening. Citation Format: Zheng B, Hollingsworth AB, Tan MY, Stough RG, Liu H. Improving efficacy of applying breast MRI to detect mammography-occult breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-02-06.