A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma.

Journal: Computer methods and programs in biomedicine
PMID: 39342876

Abstract

BACKGROUND AND OBJECTIVE: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients' follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project.

Authors

  • Gian Maria Zaccaria
  • Francesco Berloco
  • Domenico Buongiorno
    Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.
  • Antonio Brunetti
    Dipartimento di Ingegneria Elettrica e dell'Informazione, Politecnico di Bari, Bari, Italy.
  • Nicola Altini
    Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona n.4, Bari 70126, Italy.
  • Vitoantonio Bevilacqua

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