EPV093a/#176 A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients

F Arezzo; V Loizzi; G Cazzato; C Lombardi; V Venerito; E Cicinelli; G Cormio; CM Santarsiero

doi:10.1136/ijgc-2021-IGCS.162

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EPV093a/#176 A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients

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F Arezzo Policlinico of Bary, Obstetrics and Gynecology Department, Bari, Italy PubMed articles Google scholar articles
V Loizzi University of Bari ‘Aldo Moro’, Interdisciplinar Department of Medicine, Bari, Italy PubMed articles Google scholar articles
G Cazzato Department of Emergency and Organ Transplantation, Pathology Section, Bari, Italy PubMed articles Google scholar articles
C Lombardi Policlinico of Bary, Obstetrics and Gynecology Department, Bari, Italy PubMed articles Google scholar articles
V Venerito Department of Emergency and Organ Transplantation, Rheumatology Unit, Bari, Italy PubMed articles Google scholar articles
E Cicinelli Policlinico of Bary, Obstetrics and Gynecology Department, Bari, Italy PubMed articles Google scholar articles
G Cormio Policlinico of Bary, Obstetrics and Gynecology Department, Bari, Italy PubMed articles Google scholar articles
CM Santarsiero Policlinico of Bary, Obstetrics and Gynecology Department, Bari, Italy PubMed articles Google scholar articles

Citation

Arezzo F, Loizzi V, Cazzato G, et al

EPV093a/#176 A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients

International Journal of Gynecologic Cancer 2021;31:A66.

Publication history

First published November 1, 2021.

Online issue publication

January 08, 2022

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