Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.

Journal: International journal of computer assisted radiology and surgery
PMID:

Abstract

PURPOSE: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation.

Authors

  • Ziyu She
    Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy. ziyu.she@polimi.it.
  • Aldo Marzullo
  • Michela Destito
    Department of Experimental and Clinical Medicine, University of Catanzaro, Catanzaro, Italy.
  • Maria Francesca Spadea
    Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy.
  • Riccardo Leone
    Neuroradiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
  • Nicoletta Anzalone
  • Sara Steffanoni
    Lymphoma Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
  • Federico Erbella
    Lymphoma Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
  • AndrĂ©s J M Ferreri
    Department of Hematology and Pathology, San Raffaele H. Scientific Institute, Milan, Italy.
  • Giancarlo Ferrigno
  • Teresa Calimeri
    Lymphoma Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
  • Elena De Momi