Performance of radiomics analysis in ultrasound imaging for differentiating benign from malignant adnexal masses: A systematic review and meta-analysis.

Journal: Acta obstetricia et gynecologica Scandinavica
Published Date: May 1, 2025

Abstract

INTRODUCTION: We present the state of the art of ultrasound-based machine learning (ML) radiomics models in the context of ovarian masses and analyze their accuracy in differentiating between benign and malignant adnexal masses.

Authors

  • Francesca Moro
    Institute of Obstetrics and Gynecology, Università Cattolica del Sacro Cuore, Rome, Italy. Electronic address: morofrancy@gmail.com.
  • Marianna Ciancia
    Department of Women's, Child and Public Health Sciences, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.
  • Maria Sciuto
    Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
  • Giulia Baldassari
    Radiomics G-STeP Research Core Facility, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.
  • Huong Elena Tran
    Radiomics G-STeP Research Core Facility, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.
  • Antonella Carcagnì
    Epidemiology and Biostatistics Facility, G-STeP Generator, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.
  • Anna Fagotti
    Department of Women's, Child and Public Health Sciences, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.
  • Antonia Carla Testa
    Department of Women's, Child and Public Health Sciences, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.

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