Clinical trial · Observational
Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors
Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors
- 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)
Thymic epithelial tumors are rare neoplasms in the anterior mediastinum. The cornerstone of the treatment is surgical resection. Administration of postoperative radiotherapy is usually indicated in patients with more extensive local disease, incomplete resection and/or more aggressive subtypes, defined by the WHO histopathological classification. In this classification thymoma types A, AB, B1, B2, B3, and thymic carcinoma are distinguished. Studies have shown large discordances between pathologists in subtyping these tumors. Moreover, the WHO classification alone does not accurately predict the risk of recurrence, as within subtypes patients have divergent prognoses. The investigators will develop AI models using digital pathology and relevant clinical variables to improve the accuracy of histopathological classification of thymic epithelial tumors, and to better predict the risk of recurrence. In this multicentric and international project three existing databases will be used from Rotterdam, Maastricht and Lyon. For all models one database will be used to build AI models, and the other two for external validation. The ultimate goal of this project is to develop AI models that support the pathologist in correctly subtyping thymic epithelial tumors, in order to prevent patients from under- or overtreatment with adjuvant radiotherapy.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Thymic Carcinoma | Thymus Carcinoma | CURATED_BROADER | 0.80 |
| Thymic Epithelial Tumor | Thymus Epithelial Neoplasm | ALIAS | 0.90 |
| Thymoma | Thymoma | ONTOLOGY_EXACT | 0.98 |
| Thymoma and Thymic Carcinoma | — | UNRESOLVED | — |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Artificial Intelligence Diagnostics | Diagnostic Test | — | UNRESOLVED |
| Recurrence Prediction Tool | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Patients with TET
- description
- Patients diagnosed with the following TET subtypes: * Thymoma Type A * Thymoma Type AB * Thymoma Type B1 * Thymoma Type B2 * Thymoma Type B3 * Thymic Carcinoma
- interventionNames
- Diagnostic Test: Artificial Intelligence Diagnostics
- label
- Recurrence
- description
- Patients with thymic epithelial tumors who have experienced recurrence.
- interventionNames
- Diagnostic Test: Recurrence Prediction Tool
Primary outcomes (1)
- measure
- WP1 - Databases/Data Pre-processing
- timeFrame
- M1-M18
- description
- The EMC-dataset includes 179 TET-patients classified by experienced TET-pathologists. Cases with good agreement between pathologists will be used for training AI-models. Evaluation includes digitized pathology slides assessed by an international expert-panel. The MUMC-database (137 patients) and CHUL-database (181 patients) provide additional data, including clinical variables. Relevant factors include age, gender, tumor volume, stage, completeness of resection, autoimmune disorders, and treatment details.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: Participants with specific diagnoses are eligible for inclusion in the study. The eligible diagnoses include various subtypes of thymoma and thymic carcinoma, specifically: * Thymoma A * Thymoma AB * Thymoma B1 * Thymoma B2 * Thymoma B3 * Thymic Carcinoma Inclusion is based on a consensus diagnosis with a level of agreement less than 70%. This criterion is applied during the training phase of the model. Recurrence Criteria: Participants with a documented recurrence outcome within a 5-year period are considered eligible for this aspect of the study. This criterion is primarily applied during the validation phase.
References
Publications (2)
- BACKGROUNDWolf JL, van Nederveen F, Blaauwgeers H, Marx A, Nicholson AG, Roden AC, Strobel P, Timens W, Weissferdt A, von der Thusen J, den Bakker MA. Interobserver variation in the classification of thymic lesions including biopsies and resection specimens in an international digital microscopy panel. Histopathology. 2020 Nov;77(5):734-741. doi: 10.1111/his.14167. Epub 2020 Sep 24. PMID 32506527
- BACKGROUNDMolina TJ, Bluthgen MV, Chalabreysse L, de Montpreville VT, de Muret A, Dubois R, Hofman V, Lantuejoul S, le Naoures C, Mansuet-Lupo A, Parrens M, Piton N, Rouquette I, Secq V, Girard N, Marx A, Besse B. Impact of expert pathologic review of thymic epithelial tumours on diagnosis and management in a real-life setting: A RYTHMIC study. Eur J Cancer. 2021 Jan;143:158-167. doi: 10.1016/j.ejca.2020.11.011. Epub 2020 Dec 11. PMID 33316754