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

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features that can support the discrimination of ovarian mass...

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Detalles Bibliográficos
Autores principales: Arezzo, Francesca, Cormio, Gennaro, La Forgia, Daniele, Santarsiero, Carla Mariaflavia, Mongelli, Michele, Lombardi, Claudio, Cazzato, Gerardo, Cicinelli, Ettore, Loizzi, Vera
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Gynecologic Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633520/
https://www.ncbi.nlm.nih.gov/pubmed/35532797
http://dx.doi.org/10.1007/s00404-022-06578-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633520/
https://www.ncbi.nlm.nih.gov/pubmed/35532797
http://dx.doi.org/10.1007/s00404-022-06578-1

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