AI Helps Rule Out Cancer in Dense Breasts (IMAGE)
Caption
Examples of deep Shapley additive explanations (SHAP) overlay images. Maximum intensity projection (MIP) images are on left, and MIP images with the SHAP overlay are on right. Positive SHAP values (red) show areas that contribute to a high probability of lesion presence, negative SHAP values (blue) show locations with reduced probability. (A) Sagittal MIP images of contrast-enhanced breast MRI scan of an invasive ductal carcinoma in a 57-year-old woman with Breast Imaging Reporting and Data System (BI-RADS) category 4. The deep learning (DL) model yielded a probability of lesion presence of 90%. Positive SHAP values (red) are shown to coincide with the location of the lesion (arrows). (B) Sagittal MIP images of contrast-enhanced breast MRI scan of a breast without lesions in a 53-year-old woman with BI-RADS 1 score. The DL model yielded a probability of lesion presence of 11%. Negative SHAP values (blue) are diffusely distributed in the breast region. (C) Transverse MIP images of contrast-enhanced breast MRI scan of a ductal carcinoma in situ in a 65-year-old woman with BI-RADS 4 score. The DL model yielded a probability of lesion presence of 32%—the lowest probability value among all breasts with malignant disease in our study. Positive SHAP values (red) are shown to coincide with the location of the lesion (arrows).
Credit
Radiological Society of North America
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