Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors
This multicentric observational study aims to enhance thymic epithelial tumor classification and predict recurrence risks using AI models. By analyzing digital pathology and clinical variables, the project addresses discrepancies in the current WHO classification system.

Main Questions:

Can AI models improve the accuracy of thymic epithelial tumor classification?
Do AI models better predict recurrence risks compared to the WHO classification system?

Participant Tasks:

Digital pathology analysis.
Develop AI models for precise classification and recurrence risk prediction.

Comparison Group:

Researchers will compare AI-enhanced classification with the WHO system to determine if AI models provide more precise subtyping and better predict recurrence risks.

Study Design:

This international project utilizes three databases from Rotterdam, Maastricht, and Lyon, with one database for AI model building and the other two for external validation.

Ultimate Goal:

Develop AI models supporting pathologists in accurately subtyping thymic epithelial tumors, preventing under- or overtreatment with adjuvant radiotherapy for more personalized and effective treatment.
- EligibilityCriteria: 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. - Gender: All - StdAgeList: Child, Adult, Older Adult - StudyPopulation: Study Population:  This study focuses on individuals diagnosed with thymic epithelial tumors. The study includes patients from three datasets: Erasmus MC (710 patients), Maastro (137 patients), and University Hospital Lyon (181 patients).  Additional Information:  Erasmus MC (710 patients): Includes age, gender, and diagnosis information; each patient may have multiple whole slide images. Maastro (137 patients): Each patient may have multiple whole slide images. University Hospital Lyon (181 patients): Each patient may have multiple whole slide images. - SamplingMethod: Non-Probability Sample