Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows

Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variabili...

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Published inJournal of clinical medicine Vol. 13; no. 1; p. 226
Main Authors Leo, Elisabetta, Stanzione, Arnaldo, Miele, Mariaelena, Cuocolo, Renato, Sica, Giacomo, Scaglione, Mariano, Camera, Luigi, Maurea, Simone, Mainenti, Pier Paolo
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 30.12.2023
MDPI
Subjects
Accuracy
Analysis
Artificial intelligence
Care and treatment
Development and progression
Endometrial cancer
Gynecology
Health aspects
Health risk assessment
Lymphatic system
Magnetic resonance imaging
Medical prognosis
Metastasis
Methods
MRI
Nomograms
Obstetrics
Prevention
Radiomics
Review
Risk factors
Tomography
Tumors
Uterus
United States
endometrial cancer
radiomics
MRI
artificial intelligence
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Summary:Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.
Bibliography:ObjectType-Article-2
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ISSN:2077-0383
2077-0383
DOI:10.3390/jcm13010226