An observational study in Thymic Epithelial Tumor, Thymic Carcinoma and Thymoma, sponsored by Erasmus Medical Center. Recruiting at 1 site in Netherlands. Per ClinicalTrials.gov, last updated 2024-03-27.
Sponsored by Erasmus Medical Center · Observational
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.
26 studies on the registry are indexed under Thymic epithelial tumor; 19 are open to participants now.
Browse Thymic epithelial tumor studies →Erasmus Medical Center is the lead sponsor of 478 studies on the registry; 183 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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:
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:
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.
Patients diagnosed with the following TET subtypes: * Thymoma Type A * Thymoma Type AB * Thymoma Type B1 * Thymoma Type B2 * Thymoma Type B3 * Thymic Carcinoma
Diagnostic Test: Artificial Intelligence Diagnostics
Patients with thymic epithelial tumors who have experienced recurrence.
Diagnostic Test: Recurrence Prediction Tool
AI Diagnostics uses advanced algorithms for precise histological image analysis to help diagnose disease, including subtype.
Also known as: AI Diagnostics, AI Classification
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.
WP1 - Databases/Data Pre-processing
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.
Time frame: M1-M18
WP2 - Deep Learning-Model for TET Classification and Recurrence Prediction
This outcome aims to create an AI-framework with two principal goals. First, investigate TET-subtypes using four different models emphasizing cell type, morphological structures, and a combination. Second, classify patients based on recurrence outcome within 5 years. An ablation study will be conducted with state-of-the-art deep learning classifiers (ResNet, Inception).
Time frame: M6-M32
WP3: Clinical Evaluation
AI-models 1-3 will be built and validated on the EMC-database, while AI-model 4 will be built on the MUMC+-database and validated on both. Model performance will be assessed using sensitivity, specificity, negative/positive predictive value. Decision analysis curves will quantify the clinical benefit, identifying patient groups with the largest utility.
Time frame: M6-M36
Plan to share: No
Started Aug 2023 (actual). Primary completion is estimated for Aug 2027, 48 months after the start.
How these figures are worked out, and which studies are left out of them.
Eligibility is decided by the study team. Share this record with your doctor or contact the team directly.
Contact study teamGet an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.