Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors (INTHYM)

Erasmus University

Status: Enrolling

Conditions: Thymoma; Thymic Epithelial Tumor; Thymoma and Thymic Carcinoma; Thymic Carcinoma

Treatments: Diagnostic Test: Recurrence Prediction Tool; Diagnostic Test: Artificial Intelligence Diagnostics

Study type: Observational

Funder types: Other

Identifiers

NCT06301945

Maastro Clinic

72725524 (Other Grant/Funding Number)

Details and patient eligibility

About

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.

Enrollment: 1,020 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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.

Trial design

1,020 participants in 2 patient groups

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

Treatment:

Diagnostic Test: Artificial Intelligence Diagnostics

Recurrence

Description:

Patients with thymic epithelial tumors who have experienced recurrence.

Treatment:

Diagnostic Test: Recurrence Prediction Tool