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CADx - Radiomics in Ovarian Tumors (CADx)
In women with an ovarian tumor, it is often unclear whether the tumor is benign or malignant. To differentiate, tumor markers (CA125 and CEA), a transvaginal ultrasound and, depending on the ultrasound image and the CA125 concentration, a CT scan are performed. The quality of radiological imaging in diagnosing abdominal pathology is often not accurate enough, making additional interventions no-dig for proper classification and interpretation of the tumor.
Objective: To improve accuracy for distinguishing benign from malignant disease in patients presenting with an ovarian mass by using a computer aided detection algorithm.
| Condition or disease | Intervention/treatment |
|---|---|
| Ovarian Cancer | Diagnostic Test: CT-scan algorithm |
This research focuses on improving the accuracy of the determination of the nature (benign or malignant) of ovarian tumors by making use of artificial intelligence by creating a CT-scan algorithm. This because a correct preoperative classification of ovarian tumors is essential for appropriate treatment. Existing prediction models often lead to unnecessary referrals to gynecological oncology hospitals, resulting in higher costs and increased stress for the patient. It is therefore important to evaluate other strategies to differentiate between benign and malignant ovarian tumors.
Artificial Intelligence (AI) for radiology is currently being developed by the Eindhoven University of Technology (TU/e) and Philips Research Europe and may provide a potential solution to this problem.
The currently developed algorithm (CADx), using a support vector machine (SVM), showed within a small population of about 100 patients a sensitivity of 74% and specificity of 74%. These are promising results to train this algorithm even further with more CT-scans images and the addition of clinical variables and even liquid biopsies.
Type of study: Retrospective study cohort This is a retrospective analysis on known data in which definitive patients diagnosis has already been established and current analysis will not affect treatment plan.
No products for patients are used, only computer aided diagnosis is used on existing radiological imaging, namely CT-scans.
This study is linked to two other Dutch trials in which ovarian tumor biomarkers are assessed in order to find out the origin of ovarian tumors preoperatively.
The first is the HE4-prediction study, with local protocol ID NL58253.031.16. The second is the OVI-DETECT study, with clinicaltrial.gov number NCT04971421.
| Study Type : | Observational |
| Estimated Enrollment : | 600 participants |
| Observational Model: | Cohort |
| Time Perspective: | Retrospective |
| Official Title: | Computer-aided Radiology for Cancer Detection and Therapy Stratification - Benign or Malignant Ovarian Tumors. |
| Actual Study Start Date : | April 5, 2021 |
| Estimated Primary Completion Date : | August 1, 2024 |
| Estimated Study Completion Date : | August 1, 2025 |
- Sensitivity and specificity of CADx algorithm [ Time Frame: 3 - 4 years ]Percentage of correct determination of malignancy by the Risk of Malignancy Index (RMI) compared to exact determination by CAD assessment in patients with an ovarian tumor
- Sensitivity and specificity of CADx algorithm with additional variables [ Time Frame: 3 - 4 years ]Correlation of the findings from CAD analysis in some patients with analysis of circulating tumor (ct) DNA and protein tumor markers or other additional clinical variables
Biospecimen Retention: Samples With DNA
| Tracking Information | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| First Submitted Date | December 13, 2021 | ||||||||
| First Posted Date | December 30, 2021 | ||||||||
| Last Update Posted Date | December 30, 2021 | ||||||||
| Actual Study Start Date | April 5, 2021 | ||||||||
| Estimated Primary Completion Date | August 1, 2024 (Final data collection date for primary outcome measure) | ||||||||
| Current Primary Outcome Measures |
Sensitivity and specificity of CADx algorithm [ Time Frame: 3 - 4 years ] Percentage of correct determination of malignancy by the Risk of Malignancy Index (RMI) compared to exact determination by CAD assessment in patients with an ovarian tumor
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| Original Primary Outcome Measures | Same as current | ||||||||
| Change History | No Changes Posted | ||||||||
| Current Secondary Outcome Measures |
Sensitivity and specificity of CADx algorithm with additional variables [ Time Frame: 3 - 4 years ] Correlation of the findings from CAD analysis in some patients with analysis of circulating tumor (ct) DNA and protein tumor markers or other additional clinical variables
|
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| Original Secondary Outcome Measures | Same as current | ||||||||
| Current Other Pre-specified Outcome Measures | Not Provided | ||||||||
| Original Other Pre-specified Outcome Measures | Not Provided | ||||||||
| Descriptive Information | |||||||||
| Brief Title | CADx - Radiomics in Ovarian Tumors | ||||||||
| Official Title | Computer-aided Radiology for Cancer Detection and Therapy Stratification - Benign or Malignant Ovarian Tumors. | ||||||||
| Brief Summary |
In women with an ovarian tumor, it is often unclear whether the tumor is benign or malignant. To differentiate, tumor markers (CA125 and CEA), a transvaginal ultrasound and, depending on the ultrasound image and the CA125 concentration, a CT scan are performed. The quality of radiological imaging in diagnosing abdominal pathology is often not accurate enough, making additional interventions no-dig for proper classification and interpretation of the tumor. Objective: To improve accuracy for distinguishing benign from malignant disease in patients presenting with an ovarian mass by using a computer aided detection algorithm. |
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| Detailed Description |
This research focuses on improving the accuracy of the determination of the nature (benign or malignant) of ovarian tumors by making use of artificial intelligence by creating a CT-scan algorithm. This because a correct preoperative classification of ovarian tumors is essential for appropriate treatment. Existing prediction models often lead to unnecessary referrals to gynecological oncology hospitals, resulting in higher costs and increased stress for the patient. It is therefore important to evaluate other strategies to differentiate between benign and malignant ovarian tumors. Artificial Intelligence (AI) for radiology is currently being developed by the Eindhoven University of Technology (TU/e) and Philips Research Europe and may provide a potential solution to this problem. The currently developed algorithm (CADx), using a support vector machine (SVM), showed within a small population of about 100 patients a sensitivity of 74% and specificity of 74%. These are promising results to train this algorithm even further with more CT-scans images and the addition of clinical variables and even liquid biopsies. Type of study: Retrospective study cohort This is a retrospective analysis on known data in which definitive patients diagnosis has already been established and current analysis will not affect treatment plan. No products for patients are used, only computer aided diagnosis is used on existing radiological imaging, namely CT-scans. This study is linked to two other Dutch trials in which ovarian tumor biomarkers are assessed in order to find out the origin of ovarian tumors preoperatively. The first is the HE4-prediction study, with local protocol ID NL58253.031.16. The second is the OVI-DETECT study, with clinicaltrial.gov number NCT04971421. |
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| Study Type | Observational | ||||||||
| Study Design | Observational Model: Cohort Time Perspective: Retrospective |
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| Target Follow-Up Duration | Not Provided | ||||||||
| Biospecimen | Retention: Samples With DNA Description:
blood based liquid biopsies such ctDNA and tumor DNA.
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| Sampling Method | Non-Probability Sample | ||||||||
| Study Population | Patients with an ovarian tumor of which it is unknown whether it is benign or malignant referred for staging laparotomy. | ||||||||
| Condition | Ovarian Cancer | ||||||||
| Intervention | Diagnostic Test: CT-scan algorithm
CADx model was developed with a Support Vector Machine (SVM) algorithm and trained using five-fold cross-validation
Other Name: Support vector machine
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| Study Groups/Cohorts | Not Provided | ||||||||
| Publications * | Not Provided | ||||||||
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* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline. |
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| Recruitment Information | |||||||||
| Recruitment Status | Recruiting | ||||||||
| Estimated Enrollment |
600 | ||||||||
| Original Estimated Enrollment | Same as current | ||||||||
| Estimated Study Completion Date | August 1, 2025 | ||||||||
| Estimated Primary Completion Date | August 1, 2024 (Final data collection date for primary outcome measure) | ||||||||
| Eligibility Criteria |
Inclusion Criteria:
Exclusion Criteria:
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| Sex/Gender |
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| Ages | 18 Years and older (Adult, Older Adult) | ||||||||
| Accepts Healthy Volunteers | Not Provided | ||||||||
| Contacts |
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| Listed Location Countries | Netherlands | ||||||||
| Removed Location Countries | |||||||||
| Administrative Information | |||||||||
| NCT Number | NCT05174377 | ||||||||
| Other Study ID Numbers | RADIOMICS vs 3 November 2019 | ||||||||
| Has Data Monitoring Committee | No | ||||||||
| U.S. FDA-regulated Product |
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| IPD Sharing Statement |
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| Responsible Party | Jurgen M.J. Piek, Gynaecologisch Oncologisch Centrum Zuid | ||||||||
| Study Sponsor | Gynaecologisch Oncologisch Centrum Zuid | ||||||||
| Collaborators |
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| Investigators | Not Provided | ||||||||
| PRS Account | Gynaecologisch Oncologisch Centrum Zuid | ||||||||
| Verification Date | December 2021 | ||||||||