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Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Authors :
Mireia Crispin-Ortuzar
Ramona Woitek
Marika A. V. Reinius
Elizabeth Moore
Lucian Beer
Vlad Bura
Leonardo Rundo
Cathal McCague
Stephan Ursprung
Lorena Escudero Sanchez
Paula Martin-Gonzalez
Florent Mouliere
Dineika Chandrananda
James Morris
Teodora Goranova
Anna M. Piskorz
Naveena Singh
Anju Sahdev
Roxana Pintican
Marta Zerunian
Nitzan Rosenfeld
Helen Addley
Mercedes Jimenez-Linan
Florian Markowetz
Evis Sala
James D. Brenton
Source :
Nature Communications, Vol 14, Iss 1, Pp 1-14 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract 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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.f80cba9b3dae477aa79e30f52e6184bd
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-023-41820-7