Radiomics analysis in ovarian cancer: A narrative review.

Francesca Arezzo, Vera Loizzi, Daniele La Forgia, Marco Moschetta, Alberto Stefano Tagliafico, Viviana Cataldo, Adam Abdulwakil Kawosha, Vincenzo Venerito, Gerardo Cazzato, Giuseppe Ingravallo, Leonardo Resta, Ettore Cicinelli, Gennaro Cormio

Research output: Contribution to journalArticlepeer-review

Abstract

Ovarian cancer (OC) is the second most common gynecological malignancy, accounting for about 14,000 deaths in 2020 in the US. The recognition of tools for proper screening, early diagnosis, and prognosis of OC is still lagging. The application of methods such as radiomics to medical images such as ultrasound scan (US), computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) in OC may help to realize so-called “precision medicine” by developing new quantification metrics linking qualitative and/or quantitative data imaging to achieve clinical diagnostic endpoints. This narrative review aims to summarize the applications of radiomics as a support in the management of a complex pathology such as ovarian cancer. We give an insight into the current evidence on radiomics applied to different imaging methods.

Original languageEnglish
Article number7833
JournalApplied Sciences (Switzerland)
Volume11
Issue number17
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Machine learning
  • Ovarian cancer
  • Precision medicine
  • Radiomics

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint

Dive into the research topics of 'Radiomics analysis in ovarian cancer: A narrative review.'. Together they form a unique fingerprint.

Cite this