Trial · NCT07883551
AI Analysis of CT Scans to Classify Ovarian Tumours as Benign, Borderline or Malignant Before Surgery
NCT07883551, Not yet recruiting, Catharina Ziekenhuis Eindhoven
3 sourcestrials-2026-11-12-0.5.0
Cite this record
ConductScience Library. AI Analysis of CT Scans to Classify Ovarian Tumours as Benign, Borderline or Malignant Before Surgery. Trials, record NCT07883551. Release trials-2026-11-12-0.5.0, 12 Nov 2026. https://conductscience.org/library/trials/NCT07883551
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Trial
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
- Title
- AI Analysis of CT Scans to Classify Ovarian Tumours as Benign, Borderline or Malignant Before Surgery
- NCT number
- NCT07883551
- Status
- Not yet recruiting
- Enrollment
- 1,300 participants, estimated
- Lead sponsor
- Catharina Ziekenhuis Eindhoven
- Conditions
- Ovarian Cancer, Ovarian Tumor, Borderline Ovarian Tumor
- First posted
- 2026-10-20
- Last updated
- 2026-10-20
Source ClinicalTrials.gov
Registered outcomes15
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description |
|---|---|---|---|
| Primary | Discrimination of the CADx model for three-class classification (benign, borderline, malignant): macro-averaged AUROC | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Area under the receiver operating characteristic curve (AUROC) of the SPECTRE-based CADx model on preoperative CT. Calculated per class (one-vs-rest) and macro-averaged across the three classes, with histopathology according to the 2020 WHO classification as reference standard. Reported for internal cross-validation in the development cohort and for the centre-held-out external validation cohort, with 95% confidence intervals estimated by bootstrapping. Range 0 to 1, higher values indicate better discrimination. |
| Secondary | Sensitivity, specificity and predictive values per tumour class | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Sensitivity, specificity, positive predictive value and negative predictive value of the CADx model for each class (one-vs-rest), against histopathology. Decision thresholds are set in the development cohort and fixed before external validation. Reported with 95% confidence intervals for internal and external validation. |
| Secondary | F1-score, balanced accuracy and confusion matrix for three-class classification | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Per-class and macro-averaged F1-score, balanced accuracy and the full three-by-three confusion matrix of predicted versus histopathological class, for internal and external validation. |
| Secondary | Calibration of predicted class probabilities | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Agreement between predicted probabilities and observed histopathological outcomes, assessed with calibration curves and the Brier score, before and after any recalibration (for example Platt or temperature scaling). Reported for internal and external validation. |
| Secondary | Net benefit of CADx-supported triage (decision curve analysis) | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Net benefit of using the CADx model to identify borderline or malignant tumours across a range of clinically relevant risk thresholds, compared with treat-all and treat-none strategies and with IOTA-ADNEX where available. |
| Secondary | Diagnostic performance of the CADx model compared with IOTA-ADNEX | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Paired comparison in patients with an available or reconstructable ADNEX result. ADNEX risk categories are collapsed to benign, borderline and malignant. Compared by AUROC (DeLong test), sensitivity and specificity (McNemar's test), Brier score and agreement (kappa). |
| Secondary | Diagnostic performance of the CADx model compared with the Risk of Malignancy Index (RMI) | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Paired comparison in patients with a calculable RMI. Because RMI is a binary score, comparison is made for benign versus borderline or malignant tumours at the threshold used in routine practice (RMI ≥200). Compared by AUROC (DeLong test) and sensitivity and specificity (McNemar's test). |
| Secondary | Diagnostic performance of the CADx model compared with the radiologist's CT report | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Paired comparison with the classification derived from the original clinical CT report using a predefined mapping rule. Compared by sensitivity, specificity (McNemar's test) and agreement (kappa). |
| Secondary | Inter-centre generalisability of CADx performance | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Macro-averaged AUROC, sensitivity and specificity per centre, and the difference between internal cross-validated and external validation performance, to quantify domain shift related to centre, scanner and acquisition protocol. |
| Secondary | CADx performance by histological subtype (exploratory) | Preoperatively (CT scan acquired before surgery), compared with histopathology of the tumour removed at surgery | Proportion of correctly classified tumours and class-specific sensitivity per histological subtype (2020 WHO classification), for example serous, mucinous, endometrioid, clear cell and non-epithelial tumours. |
1 to 10 of 15Source ClinicalTrials.gov
See all 15 registered outcomesOutcomes0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
None in this release.
Results by group0
Each measurement the trial posted, group by group, in the words and numbers it posted.
None in this release.
Participant flow0
How many started, completed or left each group, period by period.
None in this release.
Adverse events0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
None in this release.
Adverse event totals0
The totals the trial posted for each group.
None in this release.
Papers2
The papers the trial lists on ClinicalTrials.gov.
Grants0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
None in this release.
Trial comparisons0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
None in this release.
Tests0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
None in this release.
Data dates
The day each source was copied for this release.
Sources 3
- ClinicalTrials.gov's posted results, through CTTI's AACTFreely available. Acknowledgement requested
- ClinicalTrials.govNo license stated
- NIH RePORTER's clinical studies and project filesPublic domain
Each source's terms
- ClinicalTrials.gov's posted results, through CTTI's AACTClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299
- ClinicalTrials.govnone stated
- NIH RePORTER's clinical studies and project filesPublic domain (NIH RePORTER)
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
For software
Read this trial from software.
The record and each of its lists are JSON, twenty rows a page. No key.
GET https://conductscience.org/api/trials/v1/NCT07883551
GET https://conductscience.org/api/trials/v1/NCT07883551/registered