Mortality risk prediction for primary appendiceal cancer.

Journal: Surgery
PMID: 38494390

Abstract

BACKGROUND: Accurately predicting survival in patients with cancer is crucial for both clinical decision-making and patient counseling. The primary aim of this study was to generate the first machine-learning algorithm to predict the risk of mortality following the diagnosis of an appendiceal neoplasm.

Authors

  • Nolan M Winicki
    Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Shannon N Radomski
    Colorectal Research Unit, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Yusuf Ciftci
    Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Ahmed H Sabit
    Department of Biostatistics, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Fabian M Johnston
    Johns Hopkins University, Baltimore, Maryland.
  • Jonathan B Greer
    Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD. Electronic address: jgreer13@jhmi.edu.

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