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Abstract #0873

Optimizing peritumoral features on radiomic analysis of breast cancer

Jie Ding1, Karl Spuhler1, Mario Serrano Sosa1, Chunling Liu2, and Chuan Huang1,3,4,5

1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, 2Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, China, 3Radiology, Stony Brook Medicine, Stony Brook, NY, United States, 4Computer Science, Stony Brook University, Stony Brook, NY, United States, 5Psychiatry, Stony Brook Medicine, Stony Brook, NY, United States

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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