Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.

Abstract

Authors

• Liming Shi
  Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
• Yang Zhang
  Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
• Ke Nie
  Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States.
• Xiaonan Sun
  Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. Electronic address: sunxiaonan@zju.edu.cn.
• Tianye Niu
  Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
• Ning Yue
  Department of Radiation Oncology, Rutgers-The State University of New Jersey, New Brunswick, NJ, USA.
• Tiffany Kwong
  Department of Radiological Sciences, John Tu and Thomas Yuen Center for Functional Onco-Imaging, University of California, 164 Irvine Hall, Irvine, CA, 92697-5020.
• Peter Chang
  Department of Urology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
• Daniel Chow
  Department of Radiological Sciences, John Tu and Thomas Yuen Center for Functional Onco-Imaging, University of California, 164 Irvine Hall, Irvine, CA, 92697-5020.
• Jeon-Hor Chen
  Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan and Tu and Yuen Center for Functional Onco-Imaging and Department of Radiological Science, University of California, Irvine, California 92697.
• Min-Ying Su
  Department of Radiological Sciences, University of California, Irvine, CA 92697, USA.

Keywords

External Resources