Explainable Machine Learning Model to Predict Overall Survival in Patients Treated With Palliative Radiotherapy for Bone Metastases.

Journal: JCO clinical cancer informatics
PMID: 38917384

Abstract

PURPOSE: The estimation of prognosis and life expectancy is critical in the care of patients with advanced cancer. To aid clinical decision making, we build a prognostic strategy combining a machine learning (ML) model with explainable artificial intelligence to predict 1-year survival after palliative radiotherapy (RT) for bone metastasis.

Authors

  • Savino Cilla
    Medical Physics Unit, Gemelli Molise Hospital, Catholic University of Sacred Heart, 86100 Campobasso, Italy.
  • Romina Rossi
    Palliative Care Unit, IRCCS Istituto Romagnolo Studio Tumori "Dino Amadori", Meldola, Italy.
  • Ragnhild Habberstad
    Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
  • Pål Klepstad
    Department of Cancer Research and Molecular Medicine, European Palliative Care Research Centre, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. Pal.klepstad@ntnu.no.
  • Monia Dall'Agata
    Unit of Biostatistics and Clinical Trials, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy.
  • Stein Kaasa
    Department of Oncology, Oslo University Hospital, Oslo, Norway.
  • Vanessa Valenti
    Palliative Care Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy.
  • Costanza M Donati
    Radiation Oncology, Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy.
  • Marco Maltoni
    Medical Oncology Unit, Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
  • Alessio G Morganti
    Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.

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