Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion.
Humans
B7-H1 Antigen
/ metabolism
Lung Neoplasms
/ diagnostic imaging
Positron Emission Tomography Computed Tomography
/ methods
Male
Female
Carcinoma, Non-Small-Cell Lung
/ diagnostic imaging
Middle Aged
Aged
Deep Learning
Fluorodeoxyglucose F18
Adult
ROC Curve
Aged, 80 and over
Tomography, X-Ray Computed
/ methods
Deep learning
Immunotherapy response
Medical imaging
Multi-modal fusion
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
19 Jul 2024
19 Jul 2024
Historique:
received:
26
01
2024
accepted:
01
07
2024
medline:
20
7
2024
pubmed:
20
7
2024
entrez:
19
7
2024
Statut:
epublish
Résumé
Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and
Identifiants
pubmed: 39030240
doi: 10.1038/s41598-024-66487-y
pii: 10.1038/s41598-024-66487-y
doi:
Substances chimiques
B7-H1 Antigen
0
CD274 protein, human
0
Fluorodeoxyglucose F18
0Z5B2CJX4D
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16720Subventions
Organisme : This work was partly funded by the ERA-Net CHIST-ERA grant [CHIST-ERA-19-XAI-007] long term challenges in ICT project INFORM
ID : 93603
Informations de copyright
© 2024. The Author(s).
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