AI-based prediction of lesion occurrence in high-risk women based on anomalies detected in follow-up examinations

Bianca Burger; Maria Bernathova; Thomas Helbich; Christian F. Singer; Georg Langs

doi:10.1117/12.2564313

22 May 2020 AI-based prediction of lesion occurrence in high-risk women based on anomalies detected in follow-up examinations

Bianca Burger, Maria Bernathova, Thomas Helbich, Christian F. Singer, Georg Langs

Author Affiliations +

Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 115130P (2020) https://doi.org/10.1117/12.2564313
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium

Abstract

Breast Magnetic Resonance Imaging (MRI) is recognized as the most sensitive imaging method for the early detection of breast cancer in women who carry a lifetime risk for breast cancer higher than or equal to 20%. Given the aggressive biology of cancers in this population, early detection is crucial for a favorable prognosis. This study aimed to use artificial intelligence for the detection of lesions at the earliest stage in high-risk women. A Generative Adversarial Network (GAN) detected lesions in breast MR data by quantifying anomaly as divergence from healthy breast tissue appearance. First, follow-up images of patients were aligned and the breast was segmented automatically. Then, the GAN created a model of healthy variability of appearance change during follow-up in 64x64-sized image patches sampled only at healthy tissue locations in follow-up image sequences. During the assessment of new data, each image position was compared with the model yielding an anomaly score. On a image patch level, we evaluated if this anomaly score identifies confirmed lesions, as well as lesionfree regions, where lesions appear during later follow-up studies. In the first experiment of lesion detection, a mean sensitivity of 99.5% and a mean specificity of 84% was achieved. When applying the model to studies denoted as lesion-free, subsequently occurring lesions were predicted with a mean sensitivity of 92.7% and a mean specificity of 78.8%.

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Bianca Burger, Maria Bernathova, Thomas Helbich, Christian F. Singer, and Georg Langs "AI-based prediction of lesion occurrence in high-risk women based on anomalies detected in follow-up examinations", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115130P (22 May 2020); https://doi.org/10.1117/12.2564313

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KEYWORDS

Breast

Tissues

Data modeling

Image segmentation

Magnetic resonance imaging

Image registration

Breast cancer

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