Ovarian Cancer Radiomics Approach in CT Led Evaluation

NCT06817174RecruitingOBSERVATIONAL

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.

Key Facts

Lead Sponsor

Imperial College Healthcare NHS Trust

Enrollment

168

Start Date

2025-02-10

Completion Date

2032-01-01

Study Type

OBSERVATIONAL

Official Title

Prospective Validation of CT Based Radiomic Models to Predict Surgical and Clinical Outcomes in Advanced Epithelial Ovarian Cancer

Conditions

Ovarian Cancer

Eligibility

Age Range

18 Years+

Sex

FEMALE

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

Outcome Measures

Primary Outcomes

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

Time frame: 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

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

Comparison of each CT-based radiomics model concordance index to predict overall 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

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

Locations

Imperial College NHS Healthcare Trust, London, United Kingdom

Linked Papers

2021-12-18

Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC)

Abstract Background Predictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality. Methods RPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models. Results The distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06–2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56–2.62; P = 0.00647). Conclusions RPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.

Linked Investigators

Andrea G Rockall

Christina Fotopoulou

Florian Heitz

Paula Cunnea

Ovarian Cancer Radiomics Approach in CT Led Evaluation