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 ZerunianNitzan Rosenfeld, Helen Addley, Mercedes Jimenez-Linan, Florian Markowetz, Evis Sala, James D. Brenton

Risultato della ricerca: Contributo in rivistaArticolo in rivista

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.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
RivistaNature Communications
Volume14
DOI
Stato di pubblicazionePubblicato - 2023

Keywords

  • Artificial Intelligence
  • Cancer Imaging
  • Chemiotherapy
  • HGSOC
  • Machine Learning Models
  • Ovarian Cancer
  • Predictive Markers
  • RECIST
  • Radiogenomics

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