| Autor: |
Leo E; Department of Advanced Biomedical Sciences, University of Naples 'Federico II', 80131 Naples, Italy., Stanzione A; Department of Advanced Biomedical Sciences, University of Naples 'Federico II', 80131 Naples, Italy., Miele M; Department of Advanced Biomedical Sciences, University of Naples 'Federico II', 80131 Naples, Italy., Cuocolo R; Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy., Sica G; Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy., Scaglione M; Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy., Camera L; Department of Advanced Biomedical Sciences, University of Naples 'Federico II', 80131 Naples, Italy., Maurea S; Department of Advanced Biomedical Sciences, University of Naples 'Federico II', 80131 Naples, Italy., Mainenti PP; Institute of Biostructures and Bioimaging of the National Council of Research (CNR), 80131 Naples, Italy. |
| Abstrakt: |
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. |
