Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.

Abstract

Authors

• Shiba Kuanar
  Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA.
• Jason Cai
  Radiology Informatics Lab, Department of Radiology, Mayo Clinic, Rochester, Minnesota.
• Hirotsugu Nakai
  Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate, School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan. Electronic address: nakai.hirotsugu.33x@kyoto-u.jp.
• Hiroki Nagayama
  Department of Radiology, Mayo Clinic, Rochester, MN, USA.
• Hiroaki Takahashi
  Department of Radiology, Mayo Clinic, Rochester, MN, USA.
• Jordan LeGout
  Department of Radiology, Mayo Clinic, Jacksonville, FL, USA.
• Akira Kawashima
  Department of Radiology, Mayo Clinic in Arizona, Phoenix, Arizona.
• Adam Froemming
  Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
• Lance Mynderse
  Mayo Clinics, Rochester, MN.
• Chandler Dora
  Department of Urology, Mayo Clinic, Jacksonville, FL, USA.
• Mitchell Humphreys
  Department of Urology, Mayo Clinic in Arizona, Phoenix, AZ, 85054, USA.
• Jason Klug
  Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
• Panagiotis Korfiatis
  From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905.
• Bradley Erickson
  Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
• Naoki Takahashi
  1 Department of Radiology, Radiology Informatics Laboratory, Mayo Clinic, 3507 17th Ave NW, Rochester, MN 55901.

Keywords

External Resources