Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors

Recruiting

• Conditions: Thymic Epithelial Tumor; Thymic Carcinoma; Thymoma; Thymoma and Thymic Carcinoma
• Interventions: Diagnostic Test: Recurrence Prediction Tool; Diagnostic Test: Artificial Intelligence Diagnostics

• Registration Number: NCT06301945
• Lead Sponsor: Erasmus Medical Center

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-08-01
• Status Verified: 2024-03
• Study First Submitted: 2024-03-04
• Study First Submitted QC: 2024-03-04
• Study First Posted: 2024-03-08
• Last Update Submitted: 2024-03-26
• Last Update Posted: 2024-03-27
• Primary Completion: 2027-08-01
• Study Completion: 2027-08-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1020

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.

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Recurrence	Recurrence Prediction Tool	Patients with thymic epithelial tumors who have experienced recurrence.
Patients with TET	Artificial Intelligence Diagnostics	Patients diagnosed with the following TET subtypes: * Thymoma Type A * Thymoma Type AB * Thymoma Type B1 * Thymoma Type B2 * Thymoma Type B3 * Thymic Carcinoma

Primary Outcome Measures

Name	Time	Method
WP1 - Databases/Data Pre-processing	M1-M18	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.

Secondary Outcome Measures

Name	Time	Method
WP2 - Deep Learning-Model for TET Classification and Recurrence Prediction	M6-M32	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).

Trial Locations

Locations (1)

Erasmus MC; 🇳🇱 Rotterdam, South Holland, Netherlands