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| Name | Class |
|---|---|
| National Cancer Center, Korea | OTHER_GOV |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Suspected / confirmed advanced epithelial ovarian cancer |
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| Measure | Description | Time Frame |
|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Correlating radiomics model with BRCA and HRD | Correlation of each radiomics model with clinically approved biomarkers BRCA and HRD status in advanced epithelial 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. |
| Radiogenomic evaluation |
Inclusion Criteria:
Exclusion Criteria:
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Christina Fotopoulou, MD, PhD | Contact | (+44) +44 (0)20 3313 3274 | c.fotopoulou@imperial.ac.uk |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Imperial College NHS Healthcare Trust | Recruiting | London | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | 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 | ||
| 34923575 | 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. | |
| 30770825 |
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| ID | Term |
|---|---|
| D010051 | Ovarian Neoplasms |
| D000077216 | Carcinoma, Ovarian Epithelial |
| ID | Term |
|---|---|
| D004701 | Endocrine Gland Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D010049 | Ovarian Diseases |
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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.
| From enrolment to approximately 5 years after the last patient is enrolled, based on the final data capture at the end of follow-up. |
Evaluate the biological basis for different thresholds of radiomic scores (manual and automated) using molecular analysis of fresh tumour specimens and blood samples. |
| From enrolment to approximately 5 years after the last patient is enrolled, based on the final data capture at the end of follow-up. |
| 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. |
| D000291 |
| Adnexal Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D005833 | Genital Neoplasms, Female |
| D014565 | Urogenital Neoplasms |
| D000091662 | Genital Diseases |
| D004700 | Endocrine System Diseases |
| D006058 | Gonadal Disorders |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |