Role of Machine Learning (ML)-Based Classification Using Conventional <sup>18</sup>F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness

Role of Machine Learning (ML)-Based Classification Using Conventional <sup>18</sup>F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness

Purpose: to investigate the preoperative role of ML-based classification using conventional <sup>18</sup>F-FDG PET parameters and clinical data in predicting features of EC aggressiveness. Methods: retrospective study, including 123 EC patients who underwent <sup>18</sup>F-FD...

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Bibliographic Details
Main Authors: Carolina Bezzi, Alice Bergamini, Gregory Mathoux, Samuele Ghezzo, Lavinia Monaco, Giorgio Candotti, Federico Fallanca, Ana Maria Samanes Gajate, Emanuela Rabaiotti, Raffaella Cioffi, Luca Bocciolone, Luigi Gianolli, GianLuca Taccagni, Massimo Candiani, Giorgia Mangili, Paola Mapelli, Maria Picchio
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Cancers
Subjects:
endometrial cancer
<sup>18</sup>F-FDG PET
machine learning
prognostic value
imaging parameters
Online Access:https://www.mdpi.com/2072-6694/15/1/325

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https://www.mdpi.com/2072-6694/15/1/325

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