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

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Title: Role of Machine Learning (ML)-Based Classification Using Conventional 18 F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness.
Authors: Bezzi, Carolina1,2 (AUTHOR), Bergamini, Alice1,3 (AUTHOR), Mathoux, Gregory4 (AUTHOR), Ghezzo, Samuele1,2 (AUTHOR), Monaco, Lavinia4 (AUTHOR), Candotti, Giorgio3 (AUTHOR), Fallanca, Federico2 (AUTHOR), Gajate, Ana Maria Samanes2 (AUTHOR), Rabaiotti, Emanuela3 (AUTHOR), Cioffi, Raffaella3 (AUTHOR), Bocciolone, Luca3 (AUTHOR), Gianolli, Luigi2 (AUTHOR), Taccagni, GianLuca5 (AUTHOR), Candiani, Massimo1,3 (AUTHOR), Mangili, Giorgia3 (AUTHOR), Mapelli, Paola1,2 (AUTHOR), Picchio, Maria1,2 (AUTHOR) picchio.maria@hsr.it
Source: Cancers. Jan2023, Vol. 15 Issue 1, p325. 17p.
Database: Academic Search Ultimate
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  Data: Role of Machine Learning (ML)-Based Classification Using Conventional 18 F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness.
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  Data: <searchLink fieldCode="JN" term="%22Cancers%22">Cancers</searchLink>. Jan2023, Vol. 15 Issue 1, p325. 17p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=161190100
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              Text: Jan2023
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