Ovarian Cancer Radiomics Approach in CT Led Evaluation

NCT06817174 | Recruiting

• 1 other identifier: 25/SC/0032
• Study Type: observational
• Target: 168
• Locations: 1 country
• Sites: 1

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

Keywords

Ovarian cancer; Radiomics; CT; Prognosis; Epithelial ovarian cancer; Biomarkers

Outcome Measures

Primary Outcomes (1)

• Comparison of CT-based Radiomics Models and Clinical Model in Predicting Progression-Free Survival Post-Cytoreductive Surgery in Ovarian Cancer

  Comparison of each CT-based radiomics model concordance index to predict progression free survival against the clinical model following cytoreductive surgery in the primary or interval setting. Comparisons: i. Manual CT radiomics model to the clinical model alone ii. Automated CT radiomics model to the clinical model alone

  From enrolment to approximately 5 years after the last patient is enrolled, based on the final data capture at the end of follow-up.

Secondary Outcomes (1)

• Comparison of CT-Radiomics Models and Clinical Model in Predicting Overall Survival Post-Cytoreductive Surgery in Ovarian Cancer

  From enrolment to approximately 5 years after the last patient is enrolled, based on the final data capture at the end of follow-up.

Other Outcomes (2)

• Correlating radiomics model with BRCA and HRD

  From enrolment to approximately 5 years after the last patient is enrolled, based on the final data capture at the end of follow-up.

• Radiogenomic evaluation

  From enrolment to approximately 5 years after the last patient is enrolled, based on the final data capture at the end of follow-up.

Study Arms (1)

Suspected / confirmed advanced epithelial ovarian cancer

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population will consist of patients from Imperial College Healthcare Trust, London, UK and National Cancer Centre, Korea, diagnosed with ovarian masses suspected or confirmed as advanced epithelial ovarian cancer. Participants must be medically fit to undergo the combination of cytoreductive surgery and platinum-based chemotherapy, as part of their standard anticancer treatment.

You may qualify if:

• 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)

You may not qualify if:

• 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

Sponsors & Collaborators

• Imperial College Healthcare NHS Trust: lead
• National Cancer Center, Korea: collaborator

Study Sites (1)

Imperial College NHS Healthcare Trust

London, United Kingdom

RECRUITING

Related Publications (3)

• 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

  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.

  PMID: 34923575 BACKGROUND

• 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.

  PMID: 30770825 BACKGROUND

Biospecimen

Retention: SAMPLES WITH DNA

There is the option for patients to donate tumour tissue and blood samples. This will only occur during standard of care cytoreductive surgery and a patient has consented to having these samples collected by the research team who are also the direct healthcare team.

MeSH Terms

Conditions

Ovarian Neoplasms; Carcinoma, Ovarian Epithelial

Condition Hierarchy (Ancestors)

Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Ovarian Diseases; Adnexal Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Neoplasms, Female; Urogenital Neoplasms; Genital Diseases; Endocrine System Diseases; Gonadal Disorders; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type

Central Study Contacts

Christina Fotopoulou, MD, PhD

CONTACT

(+44) +44 (0)20 3313 3274; c.fotopoulou@imperial.ac.uk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 4, 2025
• First Posted: February 10, 2025
• Study Start: February 10, 2025
• Primary Completion (Estimated): January 1, 2032
• Study Completion (Estimated): January 1, 2032
• Last Updated: June 27, 2025

Record last verified: 2025-06

Locations

GB (1)