A Novel Machine Learning-based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator.

Journal: Urology
PMID:

Abstract

OBJECTIVE: To create a machine-learning predictive model combining prostate imaging-reporting and data system (PI-RADS) score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa).

Authors

  • Flavio Vasconcelos Ordones
    Tauranga Public Hospital, Tauranga, Bay of Plenty, New Zealand; University of Auckland, Auckland, New Zealand; Urology Department, UNESP, São Paulo State University, Botucatu, SP, Brazil. Electronic address: fvordones@gmail.com.
  • Paulo Roberto Kawano
    Urology Department, UNESP, São Paulo State University, Botucatu, SP, Brazil.
  • Lodewikus Vermeulen
    Tauranga Public Hospital, Tauranga, Bay of Plenty, New Zealand.
  • Ali Hooshyari
    Tauranga Public Hospital, Tauranga, Bay of Plenty, New Zealand.
  • David Scholtz
    Tauranga Public Hospital, Tauranga, Bay of Plenty, New Zealand.
  • Peter John Gilling
    Tauranga Public Hospital, Tauranga, Bay of Plenty, New Zealand; University of Auckland, Auckland, New Zealand.
  • Darren Foreman
    College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia.
  • Basil Kaufmann
    Department of Urology University Hospital Zurich Zurich Switzerland.
  • Cédric Poyet
    Department of Urology University Hospital Zurich Zurich Switzerland.
  • Michael Gorin
    Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY.
  • Abner Macola Pacheco Barbosa
    Department of Internal Medicine, UNESP, São Paulo State University, Botucatu, SP, Brazil.
  • Naila Camila da Rocha
    Laboratory of Data Science and Predictive Analysis in Health, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil.
  • Luís Gustavo Modelli de Andrade
    Department of Internal Medicine, Botucatu Medical School, University of São Paulo State-UNESP, Avenida Professor Mario Rubens Montenegro, Botucatu, São Paulo, 18618-687, Brazil.