Order Article Reprints

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

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

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