The objective of this study is to explore the integration of in vivo and ex vivo of MRI with histology and molecular assessments to advance non-invasive characterization of tumor heterogeneity in high-grade serous ovarian cance
Patient aged >18
Pathologically proven HGSOC at an advanced stage (FIGO IIIB or IIIC) which can benefit from surgery with or without prior neoadjuvant treatment
Willingness and ability to comply with planned visits, treatment plan, laboratory tests and other study procedures,
Patient who has given informed, written and express consent,
Early-stage disease (FIGO <IIIB) or presence of extraperitoneal metastases,
Patient who will not have surgery
Patient whose regular follow-up is impossible for psychological, family, social or geographical reasons,
Patient under guardianship, curatorship or safeguarding of justice,
Single group
Blood sample and tissue sample
Biological/VaccineDuring the surgery : * Tissus sample : primary tumor and metastasis * blood sample : 3 EDTA tubes * ex vivo MRI data
the integration of in vivo and ex vivo of MRI with histology and molecular assessments to advance non-invasive characterization of tumor heterogeneity in high-grade serous ovarian cancer.
The diagnostic performance of the radiomic and multiomic algorithm for the characterization of heterogeneity in the CSOHG.
the imaging phenotype of tumor heterogeneity with a multi-scale radiomic approach by obtaining the image mirror tumor at the in vivo scale
Correlation between radiomic maps and pathogenic maps of heterogeneity,
tumor heterogeneity based on phenotypic imaging supported by AI reflects and can predict sub-histologyunderlying by tumor stroma proportion and tumor density infiltrating lymphocytes and genomics through HRD
Correlation between radiomic algorithms and i/underlying histology and ii/ genomics
the heterogeneity of the tumor biology of CSOHG through non-invasive habitat imaging combined with an integrated Multi-O-Mics approach.
Correlation between radiomic maps and tumor biology (CYTOF, proteomics and transcriptomics),
To Correlate MRI results with hematological molecular biology results.
Correlation between radiomic algorithms for tumor detection and cDNA determination.
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