Assessing Histology Structures by
MRI
histology
machine learning
ovarian cancer
radiomics
Journal
Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867
Informations de publication
Date de publication:
2021
2021
Historique:
received:
07
09
2021
accepted:
02
12
2021
entrez:
7
2
2022
pubmed:
8
2
2022
medline:
8
2
2022
Statut:
epublish
Résumé
The value of MR radiomic features at a microscopic scale has not been explored in ovarian cancer. The objective of this study was to probe the associations of MR microscopy (MRM) images and MRM-derived radiomic maps with histopathology in high-grade serous ovarian cancer (HGSOC). Nine peritoneal implants from 9 patients with HGSOC were imaged
Identifiants
pubmed: 35127479
doi: 10.3389/fonc.2021.771848
pmc: PMC8807492
doi:
Types de publication
Journal Article
Langues
eng
Pagination
771848Subventions
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Informations de copyright
Copyright © 2022 Tardieu, Lakhman, Khellaf, Cardoso, Sgarbura, Colombo, Crispin-Ortuzar, Sala, Goze-Bac and Nougaret.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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