A machine learning approach for predicting textbook outcome after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy.

Journal: World journal of surgery
PMID: 38651936

Abstract

INTRODUCTION: Peritoneal carcinomatosis is considered a late-stage manifestation of neoplastic diseases. Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) can be an effective treatment for these patients. However, the procedure is associated with significant morbidity. Our aim was to develop a machine learning model to predict the probability of achieving textbook outcome (TO) after CRS-HIPEC using only preoperatively known variables.

Authors

  • Amir Ashraf Ganjouei
    Department of Surgery, University of California, San Francisco, San Francisco, California, United States.
  • Fernanda Romero-Hernandez
    Department of Surgery, University of California San Francisco, San Francisco, California, USA.
  • Jaeyun Jane Wang
    Department of Surgery, University of California, San Francisco, California, USA.
  • Ahmed Hamed
    Division of Surgical Oncology, Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
  • Ahmed Alaa
    Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
  • David Bartlett
    Department of Surgery, Allegheny Health Network, Pittsburgh, Pennsylvania, USA.
  • Adnan Alseidi
    Department of Surgery, University of California, San Francisco, CA, USA.
  • Mohammad Haroon Choudry
    Division of Surgical Oncology, Department of Surgery, UPMC Cancer Pavilion, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
  • Mohamed Adam
    Department of Surgery, University of California, San Francisco, San Francisco, California, United States.

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