Prognostic Value of [
[18F]FDG-PET/CT
gastric cancer
machine learning
radiomics
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
23 May 2023
23 May 2023
Historique:
received:
13
03
2023
revised:
13
05
2023
accepted:
14
05
2023
medline:
10
6
2023
pubmed:
10
6
2023
entrez:
10
6
2023
Statut:
epublish
Résumé
To improve identification of peritoneal and distant metastases in locally advanced gastric cancer using [ [ None of the models could identify metastases with low AUCs of 0.59, 0.51, and 0.56, for the clinical, radiomic, and clinicoradiomic model, respectively. Subgroup analysis of intestinal and mixed-type tumours resulted in low AUCs of 0.67 and 0.60 for the clinical and radiomic models, and a moderate AUC of 0.71 in the clinicoradiomic model. Subgroup analysis of diffuse-type tumours did not improve the classification performance. Overall, [
Identifiants
pubmed: 37296837
pii: cancers15112874
doi: 10.3390/cancers15112874
pmc: PMC10251816
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Netherlands Organisation for Health Research and Development
ID : 843004103
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