Development of Machine Learning Models to Predict Tumor Endoprosthesis Survival.

Journal: Journal of surgical oncology
Published Date: Jun 12, 2025

Abstract

BACKGROUND AND OBJECTIVES: Endoprosthetic reconstruction is the preferred approach for limb salvage surgery for many patients following malignant bone tumor resection. Implant failure is a common complication, however, there are no reliable means with which to offer patient-specific survival estimations. Implant survival predictions can set patient expectations and may guide treatment planning. This study aims to test and compare machine-learning models for the prediction of early tumor endoprosthetic implant survival.

Authors

  • Barlas Goker
    Department of Orthopedic Surgery, Montefiore Medical Center, Bronx, NY, USA.
  • Andrew Brook
    Albert Einstein College of Medicine, Bronx, New York, USA.
  • Ranxin Zhang
    Department of Orthopedic Surgery, Montefiore Medical Center, Bronx, New York, USA.
  • Boudewijn Aasman
    Montefiore Medical Center and Albert Einstein College of Medicine, Center for Health Data Innovations, Bronx, New York, USA.
  • Jichuan Wang
    Department of Orthopedic Surgery, Montefiore Medical Center, Bronx, New York, USA.
  • Alexander Ferrena
    Albert Einstein College of Medicine, Institute for Clinical and Translational Research, Bronx, New York, USA.
  • Parsa Mirhaji
    Albert Einstein College of Medicine, 1300 Morris Park Ave, Bronx, NY 10461, United States of America.
  • Rui Yang
    Department of Biomedical Informatics, Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore.
  • Bang H Hoang
    Department of Orthopedic Surgery, Montefiore Medical Center, Bronx, New York, USA.
  • David S Geller
    Department of Orthopedic Surgery, Montefiore Medical Center, Bronx, New York, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals