Radiomics Analysis in Ovarian Cancer: A Narrative Review

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

doi:10.3390/app11177833

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.

Highlights

Introduction published maps and institutional affilRecently, there has been increased interest in AI techniques applied to radiomic analysis
This paper aims to analyze the application of radiomics to different imaging techniques in the study of ovarian cancer, starting by reviewing the role of radiomics in ultrasound, in magnetic resonance imaging (MRI), and in computed tomography (CT)
This study showed a good correlation between some radiomic features and prognosis, verifying an AUC of 0.87 [81]

Summary

Introduction

There has been increased interest in AI techniques applied to radiomic analysis. Radiomic analysis transforms large numbers of quantitative imaging features (i.e., shape, texture, signal intensity, and wavelength features) into mineable data by machine learning tools [2,3,4,5]. Such information could be applied in creating descriptive and predictive models that are capable of providing diagnosis and prognosis in different tumors and serve as useful decision support tools [6]. Recent studies in oncology have shown the ability of radiomics in the study of various tumor classification and staging methods [7], the prediction of biological features of tumors [8], the risk of lymph node metastasis [9], disease-free survival rates [10], reoccurrence risk [11], neoadjuvant chemotherapy [12], and chemoradiation response [13]. iations.

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