1. OC10.01: *Developing and validating ultrasound‐based radiomics models for predicting high‐risk category in endometrial cancer patients. Issue Volume 58:Issue S1(2021)Supplement (14th October 2021) Authors: Moro, F.; Albanese, M.; Boldrini, L.; Chiappa, V.; Lenkowicz, J.; Bertolina, F.; Mascilini, F.; Moroni, R.; Gambacorta, M.; Raspagliesi, F.; Scambia, G.; Testa, A.C.; Fanfani, F. Journal: Ultrasound in obstetrics & gynecology Issue: Volume 58:Issue S1(2021)Supplement Page Start: 31 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
2. VP66.24: The adoption of radiomics and machine learning improves the diagnostic processes of women with ovarian masses (aroma study). (15th October 2020) Authors: Chiappa, V.; Bogani, G.; Bertolina, F.; Interlenghi, M.; Salvatore, C.; Signorelli, M.; Castiglioni, I.; Raspagliesi, F. Journal: Ultrasound in obstetrics & gynecology Issue: Volume 56(2020)Supplement 1 Page Start: 371 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗