Clinical trial · Observational
Improving Pancreatic Cancer Care by the Use of Computational Science and Technology
NCT06055010CI-TRIAL-00069938IMPACTrecruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The goal of the IMPACT project is to set up a data sharing infrastructure between expert centers for pancreatic surgery that enables training, testing and validation of computer science tools to improve quality of care for patients with pancreatic cancer.
Conditions
Conditions (6)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Pancreas Adenocarcinoma | Pancreatic Adenocarcinoma | ALIAS | 0.90 |
| Pancreas Cyst | — | UNRESOLVED | — |
| Pancreatic Adenocarcinoma | Pancreatic Adenocarcinoma | ONTOLOGY_EXACT | 0.98 |
| Pancreatic Cancer | Malignant Pancreatic Neoplasm | CURATED_EXACT | 0.92 |
| Pancreatic Cyst | — | UNRESOLVED | — |
| Pancreatic Diseases | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| No interventions | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Healthy individuals
- description
- Healthy individuals who received an abdominal CT-scan (controls).
- interventionNames
- Other: No interventions
- label
- Individuals with pancreatic lesions
- description
- Patients who received diagnostic procedures and/or treatment for (suspected) benign and malignant pancreatic lesions as registered in the Dutch Pancreatic Cancer Project (PACAP) audit database
- interventionNames
- Other: No interventions
Primary outcomes (1)
- measure
- Subproject dependent
- timeFrame
- Through study completion, an average of 5 years.
- description
- Endpoints within this project are dependent on the specific subprojects. This study will facilitate the collection of large amounts of real-world data for (future) computer science projects.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * ≥18 years of age * Patients who received diagnostic procedures and/or treatment for (suspected) benign and malignant pancreatic lesions as registered in the Dutch Pancreatic Cancer Project (PACAP) audit database and healthy individuals who received an abdominal CT-scan (controls) Exclusion Criteria: \- Subjects who object to the use of their data for the purpose of scientific research
References
Publications (1)
- DERIVEDBohm D, Andel PCM, Akkermans PA, Boekestijn B, van der Geest W, de Haas RJ, Kist JW, Molenaar IQ, Nederend J, Nio CY, Pranger BK, van Santvoort HC, Struik F, Verpalen IM, Wessels FJ, Veldhuis WB, Verkooijen HM, Willemssen FEJA, Zoetekouw RI, Dijkstra J, Intven MPW, Weinmann M, Daamen LA. MKNet-family architectures for auto-segmentation of the residual pancreas after pancreatic resection: a deep learning comparative study. Abdom Radiol (NY). 2026 Jul;51(7):3492-3503. doi: 10.1007/s00261-025-05211-4. Epub 2025 Nov 27. PMID 41307673