Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Artificial Intelligence Diagnostics, Recurrence Prediction Tool.
- Who it may be relevant to
- Registry conditions: Thymic Epithelial Tumor, Thymic Carcinoma, Thymoma, Thymoma and Thymic Carcinoma. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Netherlands
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors
Overview
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.
Interventions
- Diagnostic test Artificial Intelligence Diagnostics
AI Diagnostics uses advanced algorithms for precise histological image analysis to help diagnose disease, including subtype. - Diagnostic test Recurrence Prediction Tool
This AI tool evaluates thymic tumour data and other clinical data and calculates the risk of recurrence, with the aim of analysing whether there is an association with specific subtypes of thymic epithelial tumours and clinical data.
Primary outcome measures
- WP1 - Databases/Data Pre-processing [Time frame: M1-M18]
Secondary outcome measures (1)
- WP2 - Deep Learning-Model for TET Classification and Recurrence Prediction [Time frame: M6-M32]
Eligibility 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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
Netherlands · 1 center
- Erasmus MC — Rotterdam
Publications
- Wolf 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
- Molina 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 PMID 33316754
Identifiers
NCT: NCT06301945 · Maastro Clinic · 72725524