Bezzi, C.; Bergamini, A.; Mathoux, G.; Ghezzo, S.; Monaco, L.; Candotti, G.; Fallanca, F.; Gajate, A.M.S.; Rabaiotti, E.; Cioffi, R.;
et al. Role of Machine Learning (ML)-Based Classification Using Conventional 18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness. Cancers 2023, 15, 325.
https://doi.org/10.3390/cancers15010325
AMA Style
Bezzi C, Bergamini A, Mathoux G, Ghezzo S, Monaco L, Candotti G, Fallanca F, Gajate AMS, Rabaiotti E, Cioffi R,
et al. Role of Machine Learning (ML)-Based Classification Using Conventional 18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness. Cancers. 2023; 15(1):325.
https://doi.org/10.3390/cancers15010325
Chicago/Turabian Style
Bezzi, Carolina, Alice Bergamini, Gregory Mathoux, Samuele Ghezzo, Lavinia Monaco, Giorgio Candotti, Federico Fallanca, Ana Maria Samanes Gajate, Emanuela Rabaiotti, Raffaella Cioffi,
and et al. 2023. "Role of Machine Learning (ML)-Based Classification Using Conventional 18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness" Cancers 15, no. 1: 325.
https://doi.org/10.3390/cancers15010325
APA Style
Bezzi, C., Bergamini, A., Mathoux, G., Ghezzo, S., Monaco, L., Candotti, G., Fallanca, F., Gajate, A. M. S., Rabaiotti, E., Cioffi, R., Bocciolone, L., Gianolli, L., Taccagni, G., Candiani, M., Mangili, G., Mapelli, P., & Picchio, M.
(2023). Role of Machine Learning (ML)-Based Classification Using Conventional 18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness. Cancers, 15(1), 325.
https://doi.org/10.3390/cancers15010325
