Machine Learning for Predicting the Low Risk of Postoperative Pancreatic Fistula After Pancreaticoduodenectomy: Toward a Dynamic and Personalized Postoperative Management Strategy.

Journal: Cancers
Published Date: May 31, 2025

Abstract

BACKGROUND: Postoperative pancreatic fistula (POPF) remains one of the most relevant complications following pancreaticoduodenectomy (PD), significantly impacting short-term outcomes and delaying adjuvant therapies. Current predictive models offer limited accuracy, often failing to incorporate early postoperative data. This retrospective study aimed to develop and validate machine learning (ML) models to predict the absence and severity of POPF using clinical, surgical, and early postoperative variables.

Authors

  • Roberto Cammarata
    Operative Research Unit of General Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy.
  • Filippo Ruffini
    Unit of Computer Systems & Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
  • Alberto Catamerò
    Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
  • Gennaro Melone
    Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
  • Gianluca Costa
    Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea Hospital, Via di Grottarossa 1035-39, 00189, Rome, Italy.
  • Silvia Angeletti
    Unit of Laboratory, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy.
  • Federico Seghetti
    Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
  • Vincenzo La Vaccara
    Operative Research Unit of General Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy.
  • Roberto Coppola
    Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128, Roma, Italy.
  • Paolo Soda
    Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy; Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå, University, Umeå, Sweden. Electronic address: paolo.soda@umu.se.
  • Valerio Guarrasi
    Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy; Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Italy. Electronic address: valerio.guarrasi@unicampus.it.
  • Damiano Caputo
    Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128, Roma, Italy.

Keywords

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