MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

Abstract

Authors

• Almir G V Bitencourt
  Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Imaging, A.C. Camargo Cancer Center, Sao Paulo, SP, Brazil.
• Peter Gibbs
  Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
• Carolina Rossi Saccarelli
  Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Hospital Sírio-Libanês, São Paulo, SP, Brazil.
• Isaac Daimiel
  Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
• Roberto Lo Gullo
  Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.
• Michael J Fox
  Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
• Sunitha Thakur
  Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
• Katja Pinker
  Department of Radiology, Columbia University, Vagelos College of Physicians and Surgeons, New York, New York, USA.
• Elizabeth A Morris
  Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY.
• Monica Morrow
  Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
• Maxine S Jochelson
  Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Keywords

External Resources