Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer.

Journal: European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Published Date: Jul 13, 2023

Abstract

BACKGROUND: Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experimental pilot study was to develop ML models able to predict curative OC MDT treatment decisions and determine the relative importance of underlying decision-critical variables.

Authors

  • Navamayooran Thavanesan
    School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK. Electronic address: N.Thavanesan@soton.ac.uk.
  • Indu Bodala
    School of Electronics and Computer Science, University of Southampton, UK.
  • Zoë Walters
    School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK.
  • Sarvapali Ramchurn
    School of Electronics and Computer Science, University of Southampton, UK.
  • Timothy J Underwood
    School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK.
  • Ganesh Vigneswaran
    School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK.

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