Clinical trial · Observational
Ovarian Cancer Radiomics Approach in CT Led Evaluation
Prospective Validation of CT Based Radiomic Models to Predict Surgical and Clinical Outcomes in Advanced Epithelial Ovarian Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Ovarian Cancer | Malignant Ovarian Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- Suspected / confirmed advanced epithelial ovarian cancer
Primary outcomes (1)
- measure
- Comparison of CT-based Radiomics Models and Clinical Model in Predicting Progression-Free Survival Post-Cytoreductive Surgery in Ovarian Cancer
- timeFrame
- From enrolment to approximately 5 years after the last patient is enrolled, based on the final data capture at the end of follow-up.
- description
- 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
Secondary outcomes (1)
- measure
- Comparison of CT-Radiomics Models and Clinical Model in Predicting Overall Survival Post-Cytoreductive Surgery in Ovarian Cancer
- timeFrame
- From enrolment to approximately 5 years after the last patient is enrolled, based on the final data capture at the end of follow-up.
- description
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
Show eligibility criteria text
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
References
Publications (3)
- BACKGROUNDKristofer 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
- BACKGROUNDFotopoulou 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
- BACKGROUNDLu 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