Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
24 10 2023
24 10 2023
Historique:
received:
27
05
2022
accepted:
20
09
2023
medline:
26
10
2023
pubmed:
25
10
2023
entrez:
24
10
2023
Statut:
epublish
Résumé
High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.
Identifiants
pubmed: 37875466
doi: 10.1038/s41467-023-41820-7
pii: 10.1038/s41467-023-41820-7
pmc: PMC10598212
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6756Subventions
Organisme : Wellcome Trust
ID : RG98755
Pays : United Kingdom
Organisme : Department of Health
ID : BRC-1215-20014
Pays : United Kingdom
Organisme : Cancer Research UK
ID : A19274
Pays : United Kingdom
Organisme : Cancer Research UK
ID : A25117
Pays : United Kingdom
Organisme : Medical Research Council
Pays : United Kingdom
Organisme : Cancer Research UK
ID : A17197
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C9545/A29580
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 28292
Pays : United Kingdom
Organisme : Cancer Research UK
ID : A22905
Pays : United Kingdom
Organisme : Cancer Research UK
ID : A20240
Pays : United Kingdom
Organisme : Cancer Research UK
ID : A27066
Pays : United Kingdom
Organisme : Department of Health
ID : RC-1215-20014
Pays : United Kingdom
Informations de copyright
© 2023. Springer Nature Limited.
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