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Abstract #1669

Tumor segmentation with nnU-Net on dynamic contrast enhanced MR images of triple negative breast cancer

Zhan Xu1, Sanaz Pashapoor2, Bikash Panthi1, Jong Bum Son1, Ken-Pin Hwang1, Beatriz Elena Adrada2, Rosalind Pitpitan Candelaria2, Mary Saber Guirguis2, Miral Mahesh Patel2, Huong Le-Petross2, Jessica Leung2, Marion Elizabeth Scoggins2, Gary Whitman2, Rania Mohamed2, Deanna Lynn Lane2, Tanya Moseley2, Frances Perez2, Jason White3, Elizabeth Ravenberg3, Alyson Clayborn3, Huiqin Chen4, Jia Sun4, Peng Wei4, Alastair Thompson5, Anil Korkut6, Lei Huo7, Kelly Hunt8, Stacy Moulder3, Jennifer Litton3, Vicente Valero3, Debu Tripathy9, Wei Yang2, Clinton Yam3, Gaiane Margishvili Rauch2, and Jingfei Ma1
1Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States, 2Departments of Breast Imaging, MD Anderson Cancer Center, Houston, TX, United States, 3Departments of Breast Medical Oncology, MD Anderson Cancer Center, Houston, TX, United States, 4Departments of Biostatistics, MD Anderson Cancer Center, Houston, TX, United States, 5Section of Breast Surgery, Baylor College of Medicine, Houston, TX, United States, 6Departments of Bioinformatics & Computational Biology, MD Anderson Cancer Center, Houston, TX, United States, 7Departments of Pathology, MD Anderson Cancer Center, Houston, TX, United States, 8Departments of Breast Surgical Oncology, MD Anderson Cancer Center, Houston, TX, United States, 9Department of Breast Medical Oncology, MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: Cancer, Breast, TNBCQuantitative image analysis of cancers requires accurate tumor segmentation that is often performed manually. In this study, we developed a deep learning model with the self-configurable nnU-Net for automated tumor segmentation on dynamic contrast enhanced MR images of triple negative breast cancers. Our results on an independent testing dataset demonstrated that this nnU-Net based deep learning model can perform automated tumor segmentation with high sensitivity and Dice coefficient.

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Keywords