Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors

Thymoma Clinical Trial

— INTHYM  

Official title:

Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06301945
• Other study ID #: Maastro Clinic
• Secondary ID: 72725524
• Status: Recruiting
• Start date: August 1, 2023
• Est. completion date: August 1, 2027

Study information

• Verified date: March 2024
• Source: Erasmus Medical Center
• Contact: Anna Salut Esteve Domínguez
• Phone: 0107043491
• Email: a.estevedominguez[@]erasmusmc.nl
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1020
• Est. completion date: August 1, 2027
• Est. primary completion date: August 1, 2027
• Gender: All
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Carcinoma
• Neoplasms, Glandular and Epithelial
• Thymic Carcinoma
• Thymic Epithelial Tumor
• Thymoma
• Thymoma and Thymic Carcinoma
• Thymus Neoplasms

Intervention

Diagnostic Test:
• Artificial Intelligence Diagnostics
AI Diagnostics uses advanced algorithms for precise histological image analysis to help diagnose disease, including subtype.
• 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.

Locations

Country	Name	City	State
Netherlands	Erasmus MC	Rotterdam	South Holland

Sponsors (3)

Lead Sponsor	Collaborator
Erasmus Medical Center	Hospices Civils de Lyon, Maastro Clinic, The Netherlands

Country where clinical trial is conducted

Netherlands, 

References & Publications (2)

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.2020.11.011. Epub 2020 Dec 11. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	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.	M6-M36
Primary	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.	M1-M18
Secondary	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).	M6-M32