Multimodal deep learning model on interim [
Deep learning
Lymphoma
Positron emission tomography/computed tomography
Treatment failure
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Jan 2023
Jan 2023
Historique:
received:
14
03
2022
accepted:
13
07
2022
revised:
30
05
2022
pubmed:
28
8
2022
medline:
20
12
2022
entrez:
27
8
2022
Statut:
ppublish
Résumé
The prediction of primary treatment failure (PTF) is necessary for patients with diffuse large B-cell lymphoma (DLBCL) since it serves as a prominent means for improving front-line outcomes. Using interim Initially, 205 DLBCL patients undergoing interim [ The final model with contrastive objective optimization, named the contrastive hybrid learning model, performed best, with an accuracy of 91.22% and an area under the receiver operating characteristic curve (AUC) of 0.926, in the primary dataset. In the external dataset, its accuracy and AUC remained at 88.64% and 0.925, respectively, indicating its good generalization ability. The proposed model achieved good performance, validated the predictive value of interim PET/CT, and holds promise for directing individualized clinical treatment. • The proposed multimodal models achieved accurate prediction of primary treatment failure in DLBCL patients. • Using an appropriate feature-level fusion strategy can make the same class close to each other regardless of the modal heterogeneity of the data source domain and positively impact the prediction performance. • Deep learning validated the predictive value of interim PET/CT in a way that exceeded human capabilities.
Identifiants
pubmed: 36029345
doi: 10.1007/s00330-022-09031-8
pii: 10.1007/s00330-022-09031-8
doi:
Substances chimiques
Fluorodeoxyglucose F18
0Z5B2CJX4D
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
77-88Subventions
Organisme : National Natural Science Foundation of China
ID : 81974276
Organisme : National Natural Science Foundation of China
ID : 81830007
Organisme : National Natural Science Foundation of China
ID : 81520108003
Organisme : National Natural Science Foundation of China
ID : 81670176
Organisme : National Natural Science Foundation of China
ID : 82070204
Informations de copyright
© 2022. The Author(s), under exclusive licence to European Society of Radiology.
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