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

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 and James D. Brenton
Additional contact information
Mireia Crispin-Ortuzar: University of Cambridge
Ramona Woitek: University of Cambridge
Marika A. V. Reinius: University of Cambridge
Elizabeth Moore: University of Cambridge
Lucian Beer: University of Cambridge
Vlad Bura: University of Cambridge
Leonardo Rundo: University of Cambridge
Cathal McCague: University of Cambridge
Stephan Ursprung: University of Cambridge
Lorena Escudero Sanchez: University of Cambridge
Paula Martin-Gonzalez: University of Cambridge
Florent Mouliere: University of Cambridge
Dineika Chandrananda: University of Cambridge
James Morris: University of Cambridge
Teodora Goranova: University of Cambridge
Anna M. Piskorz: University of Cambridge
Naveena Singh: Barts Health NHS Trust
Anju Sahdev: Barts Health NHS Trust
Roxana Pintican: “Iuliu Hatieganu” University of Medicine and Pharmacy
Marta Zerunian: Sapienza University of Rome-Sant’Andrea University Hospital
Nitzan Rosenfeld: University of Cambridge
Helen Addley: University of Cambridge
Mercedes Jimenez-Linan: University of Cambridge
Florian Markowetz: University of Cambridge
Evis Sala: University of Cambridge
James D. Brenton: University of Cambridge

Nature Communications, 2023, vol. 14, issue 1, 1-14

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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-41820-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41820-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-41820-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().