Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

Abstract

Authors

• Amirhessam Tahmassebi
  Department of Scientific Computing, Florida State University, Tallahassee, Florida 32306-4120, United States of America.
• Georg J Wengert
  Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy.
• Thomas H Helbich
  Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy.
• Zsuzsanna Bago-Horvath
  Department of Pathology.
• Sousan Alaei
  Department of Pathology.
• Rupert Bartsch
  Division of Oncology, Department of Internal Medicine, and.
• Peter Dubsky
  Department of Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.
• Pascal Baltzer
  Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria. Electronic address: pascal.baltzer@meduniwien.ac.at.
• Paola Clauser
  Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria.
• Panagiotis Kapetas
  Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy.
• Elizabeth A Morris
  Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY.
• Anke Meyer-Baese
  2 Department of Scientific Computing, Florida State University, Tallahassee, FL, USA.
• Katja Pinker
  Department of Radiology, Columbia University, Vagelos College of Physicians and Surgeons, New York, New York, USA.

Keywords

External Resources