AI-Assisted Differentiation of Pancreatic Cystic Lesions Using EUS Images and Clinical Features
NCT07919704, Not yet recruiting, Ruijin Hospital
Trial 8
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
| Field | Value | Source |
|---|---|---|
| Title | AI-Assisted Differentiation of Pancreatic Cystic Lesions Using EUS Images and Clinical Features | ClinicalTrials.gov |
| NCT number | NCT07919704 | ClinicalTrials.gov |
| Status | Not yet recruiting | ClinicalTrials.gov |
| Enrollment | 400 participants, estimated | ClinicalTrials.gov |
| Lead sponsor | Ruijin Hospital | ClinicalTrials.gov |
| Conditions | Pancreatic Mucinous Cystic Neoplasm, Pancreatic Serous Cystic Neoplasm, Pancreatic Pseudocyst | ClinicalTrials.gov |
| First posted | 2026-11-05 | ClinicalTrials.gov |
| Last updated | 2026-11-05 | ClinicalTrials.gov |
Registered outcomes 9
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description | Source |
|---|---|---|---|---|
| Primary | Patient-Level Macro-AUROC of the Multimodal AI Model for Three-Class Classification of MCN, SCN, and PPC | At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028. | The diagnostic performance of the locked multimodal artificial intelligence (AI) model will be evaluated in an independent prospective temporal validation cohort using the patient-level macro-averaged area under the receiver operating characteristic curve (macro-AUROC). One-vs-rest AUROC will be calculated separately for mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs). The three AUROC values will then be averaged with equal weighting. Each patient will contribute one final prediction, and 95% confidence intervals will be estimated using bootstrap resampling. Higher values indicate better discrimination. | ClinicalTrials.gov |
| Secondary | Overall Patient-Level Classification Accuracy of the AI Model | At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028. | Overall accuracy will be calculated as the proportion of patients correctly classified as MCN, SCN, or PPC among all patients evaluated in the prospective validation cohort. Accuracy will be reported as a percentage with a 95% confidence interval. | ClinicalTrials.gov |
| Secondary | Macro-Averaged F1 Score of the AI Model | At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028. | The F1 score will be calculated separately for MCN, SCN, and PPC using the harmonic mean of precision and recall. The macro-F1 score will be calculated as the unweighted average of the three class-specific F1 scores. Values range from 0 to 1, with higher values indicating better classification performance. | ClinicalTrials.gov |
| Secondary | Difference in Patient-Level Macro-AUROC Between Multimodal and Unimodal AI Models | At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028. | The difference in patient-level macro-AUROC between the locked multimodal AI model and each unimodal model (EUS image-only and clinical feature-only) will be calculated in the independent prospective validation cohort. Each comparison will be reported as the arithmetic difference in macro-AUROC, with positive values indicating better discrimination by the multimodal model. | ClinicalTrials.gov |
| Secondary | Calibration Intercept of the Multimodal AI Model | At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028. | The calibration intercept of the locked multimodal AI model will be estimated in the prospective validation cohort to assess systematic overestimation or underestimation of predicted probabilities. A value closer to 0 indicates better calibration. | ClinicalTrials.gov |
| Secondary | Change in Physicians' Diagnostic Accuracy With AI Assistance | At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028. | EUS physicians with different levels of experience will evaluate cases with and without AI assistance. The primary measure for the reader study will be the paired difference in patient-level three-class diagnostic accuracy between AI-assisted and unaided interpretation. Additional reader outcomes will include macro-AUROC, macro-F1 score, diagnostic confidence, interobserver agreement, and interpretation time. | ClinicalTrials.gov |
| Secondary | Difference in Patient-Level Classification Accuracy Between Multimodal and Unimodal AI Models | At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028. | The difference in overall patient-level classification accuracy between the locked multimodal AI model and each unimodal model (EUS image-only and clinical feature-only) will be calculated in the independent prospective validation cohort. Differences will be expressed in percentage points, with positive values indicating higher accuracy for the multimodal model. | ClinicalTrials.gov |
| Secondary | Calibration Slope of the Multimodal AI Model | At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028. | The calibration slope of the locked multimodal AI model will be estimated in the prospective validation cohort to assess the agreement between the spread of predicted probabilities and observed outcomes. A value closer to 1 indicates better calibration. | ClinicalTrials.gov |
| Secondary | Multiclass Brier Score of the Multimodal AI Model | At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028. | The multiclass Brier score will be calculated as the mean, across patients, of the sum of squared differences between the predicted probabilities for MCN, SCN, and PPC and their corresponding observed class indicators. The unnormalised score ranges from 0 to 2, with lower values indicating better probabilistic predictions. | ClinicalTrials.gov |
Outcomes 0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
| Type | Outcome | Time frame | Population | Units | Measured as | Source |
|---|
Results by group 0
Each measurement the trial posted, group by group, in the words and numbers it posted.
| Outcome | Group | Category | Value | Spread | Units | Source |
|---|
Participant flow 0
How many started, completed or left each group, period by period.
| Period | Milestone | Group | Participants | Units counted | Source |
|---|
Adverse events 0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
| Type | Event | Organ system | Group | Affected | At risk | Events | Source |
|---|
Adverse event totals 0
The totals the trial posted for each group.
| Total | Group | Affected | At risk | Source |
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Papers 0
The papers the trial lists on ClinicalTrials.gov.
| PMID | Listed as | Source |
|---|
Grants 0
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.
| Project | Title | Principal investigators | Organization | Funder | Fiscal years | Source |
|---|
Trial comparisons 0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
| Comparison | Source |
|---|
Tests 0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
| Test | Named as | Source |
|---|
Source 5
| Field | Value | Source |
|---|---|---|
| Release | trials-2026-11-12-0.4.0 | ClinicalTrials.gov |
| Release manifest SHA-256 | 0f4a8d8c48aee2a7573ae0fa94ecbeab70c1adbe449bbc95c424bc7d3e97b9d4 | ClinicalTrials.gov |
| Registry capture | 2026-11-12 | ClinicalTrials.gov |
| Results copy, AACT | 2026-03-23 | ClinicalTrials.gov |
| NIH clinical studies file | 2026-11-13 | ClinicalTrials.gov |
Release trials-2026-11-12-0.4.0
ClinicalTrials.gov's posted results, through CTTI's AACT: ClinicalTrials.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.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
ClinicalTrials.gov: none stated ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
NIH RePORTER's clinical studies and project files: Public domain (NIH RePORTER)