Ovarian Cancer Radiomics Approach in CT Led Evaluation

NCT ID: NCT06817174

Last Updated: 2025-06-27

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 168 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-02-10
• Study Completion Date: 2032-01-31

Brief Summary

When patients have suspected or confirmed ovarian cancer standard treatment will involve surgery and chemotherapy. However, as with any treatment, it is challenging to predict treatment response in advance. Before treatment, all patients have a CT scan to describe where the cancer is in order to guide the treatment.

There is now a new way to analyse routine scans using advanced computing methods, which may give more information about the ovarian cancer. This is called radiomics which analyses features in scans that are not visible to the naked eye. Our group at Imperial College London has worked on developing radiomic models to better understand ovarian cancer.

This study aims to determine whether the information gained from this new approach would help us to tailor patient treatment plans to better meet the patient's individual needs, even more than done already. Furthermore, the aim is to understand how different types of ovarian cancer can correlate with the radiomic findings, which may help develop potential treatments in the future.

Conditions

Ovarian Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Suspected / confirmed advanced epithelial ovarian cancer

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• Written (signed and dated) informed consent
• Age 18 years or over
• Suspected or confirmed advanced epithelial ovarian cancer (FIGO stage 3B or more)
• Being considered for active anticancer treatment i.e. primary cytoreductive surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval cytoreductive surgery
• Evaluable baseline portal venous phase CT scan prior to surgical or medical treatment for ovarian cancer
• Disease visible on pre-treatment portal venous phase baseline CT scan (≥2cm)

Exclusion Criteria

• Known contra-indication to CT with IV contrast (e.g. contrast allergy, renal failure, inability to lie flat);
• Unable to give informed consent;
• Known pregnancy;
• No visible disease <2cm on portal venous phase baseline CT scan;
• Previous surgery for resection of an adnexal mass;
• Significant artefact on CT image for example from metal prostheses that precluded meaningful segmentation of visible disease
• Only fit for palliative care at initial presentation

• Minimum Eligible Age: 18 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

National Cancer Center, Korea

OTHER_GOV

Sponsor Role: collaborator

Imperial College Healthcare NHS Trust

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Imperial College NHS Healthcare Trust

London, , United Kingdom

Site Status: RECRUITING

Countries

United Kingdom

Central Contacts

Christina Fotopoulou, MD, PhD

Role: CONTACT

c.fotopoulou@imperial.ac.uk

(+44) +44 (0)20 3313 3274

Facility Contacts

Christina Fotopoulou, MD, PhD

Role: primary

c.fotopoulou@imperial.ac.uk

+44 (0)20 3313 3274

References

Kristofer Linton-Reid, Georg Wengert, Haonan Lu, Christina Fotopoulou, Philippa Lee, Federica Petta, Luca Russo, Giacomo Avensani, Murbarik Arshard, Philipp Harter, Mitch Chen, Marc Boubnovski, Sumeet Hindocha, Ben Hunter, Sonia Prader, Joram M. Posma, Andrea Rockall, Eric O. Aboagye. End-to-End Integrative Segmentation and Radiomics Prognostic Models Improve Risk Stratification of High-Grade Serous Ovarian Cancer: A Retrospective Multi-Cohort Study. medRxiv 2023.04.26.23289155; doi: https://doi.org/10.1101/2023.04.26.23289155

Reference Type: BACKGROUND

Fotopoulou C, Rockall A, Lu H, Lee P, Avesani G, Russo L, Petta F, Ataseven B, Waltering KU, Koch JA, Crum WR, Cunnea P, Heitz F, Harter P, Aboagye EO, du Bois A, Prader S. Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC). Br J Cancer. 2022 Apr;126(7):1047-1054. doi: 10.1038/s41416-021-01662-w. Epub 2021 Dec 18.

Reference Type: BACKGROUND

PMID: 34923575 (View on PubMed)

Lu H, Arshad M, Thornton A, Avesani G, Cunnea P, Curry E, Kanavati F, Liang J, Nixon K, Williams ST, Hassan MA, Bowtell DDL, Gabra H, Fotopoulou C, Rockall A, Aboagye EO. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Nat Commun. 2019 Feb 15;10(1):764. doi: 10.1038/s41467-019-08718-9.

Reference Type: BACKGROUND

PMID: 30770825 (View on PubMed)

Other Identifiers

25/SC/0032

Identifier Type: -

Identifier Source: org_study_id