Machine Learning Models for Predicting the Outcomes of Surgical Treatment of Colorectal Liver Metastases.

Journal: Journal of the American College of Surgeons
Published Date: Jan 19, 2023

Abstract

BACKGROUND: Surgical intervention remains the cornerstone of a multidisciplinary approach in the treatment of colorectal liver metastases (CLM). Nevertheless, patient outcomes vary greatly. While predictive tools can assist decision-making and patient counseling, decades of efforts have yet to result in generating a universally adopted tool in clinical practice.

Authors

  • Omeed Moaven
    From the Division of Surgical Oncology, Department of Surgery, Louisiana State University Health; and Louisiana State University-Louisiana Children's Medical Center Cancer Center, New Orleans, LA (Moaven).
  • Thomas E Tavolara
    Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, USA.
  • Cristian D Valenzuela
    Department of Surgical Oncology, Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC (Valenzuela, Shen).
  • Tan To Cheung
    Department of Surgery, The University of Hong Kong, Hong Kong, China.
  • Carlos U Corvera
    Department of Hepatobiliary and Pancreatic Surgery, University of California San Francisco, San Francisco, CA (Corvera).
  • Charles H Cha
    Department of Surgery, Yale School of Medicine, New Haven, CT (Cha).
  • John A Stauffer
    Division of General Surgery, Department of Surgery, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, 32224, USA.
  • Muhammad Khalid Khan Niazi
    Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
  • Metin N Gurcan
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. Electronic address: metin.gurcan@osumc.edu.
  • Perry Shen
    Department of Surgical Oncology, Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC (Valenzuela, Shen).

Keywords

External Resources

Popular Topics
Recent Journals