Recently, we developed a radiomic pipeline to non-invasively predict sentinel lymph node (SLN) metastasis in breast cancer using image features extracted from the primary tumor on the DCE-MRI. In this study, we further investigated the usefulness of the peritumoral features in the radiomic analysis and evaluated the effect of the thickness of the peritumoral regions to optimize the prediction performance. The result shows that the peritumoral features can indeed improve the prediction performance and using 4mm as the thickness of the peritumoral regions achieved the optimal prediction result in an independent validation set.
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