F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

Journal: La Radiologia medica
PMID: 39875749

Abstract

PURPOSE: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.

Authors

  • Luca Urso
    Department of Translational Medicine, University of Ferrara, Ferrara, Italy.
  • Luigi Manco
    A.O. U. di Modena, Medical Physics Unit, Modena, Italy.
  • Corrado Cittanti
    Department of Translational Medicine, University of Ferrara, Ferrara, Italy. ctc@unife.it.
  • Sara Adamantiadis
    Department of Translational Medicine, University of Ferrara, Ferrara, Italy.
  • Klarisa Elena Szilagyi
    Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
  • Giovanni Scribano
    Department of Physics and Earth Science, University of Ferrara, Ferrara, Italy.
  • Noemi Mindicini
    Oncology Unit, University Hospital of Ferrara, Ferrara, Italy.
  • Aldo Carnevale
    Department of Translational Medicine, University of Ferrara, Ferrara, Italy.
  • Mirco Bartolomei
    Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, Via Aldo Moro 8, 44124, Ferarra, Italy.
  • Melchiore Giganti
    Department of Translational Medicine, University of Ferrara, Ferrara, Italy.

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