Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors

NCT06301945 | Recruiting

• 2 other identifiers: Maastro Clinic72725524
• Study Type: observational
• Target: 1,020
• Locations: 1 country
• Sites: 1

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.

Conditions

Thymic Epithelial Tumor; Thymic Carcinoma; Thymoma; Thymoma and Thymic Carcinoma

Keywords

Artificial Intelligence; Digital pathology

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• WP2 - Deep Learning-Model for TET Classification and Recurrence Prediction

  M6-M32

Other Outcomes (1)

• WP3: Clinical Evaluation

  M6-M36

Study Arms (2)

Patients with TET

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

Recurrence

Patients with thymic epithelial tumors who have experienced recurrence.

Diagnostic Test: Recurrence Prediction Tool

Interventions

Artificial Intelligence Diagnostics DIAGNOSTIC_TEST

AI Diagnostics uses advanced algorithms for precise histological image analysis to help diagnose disease, including subtype.

Also known as: AI Diagnostics, AI Classification

Patients with TET

Recurrence Prediction Tool DIAGNOSTIC_TEST

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.

Recurrence

Eligibility Criteria

• Sex: all
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• Thymoma A
• Thymoma AB
• Thymoma B1
• Thymoma B2
• Thymoma B3
• Thymic Carcinoma
• 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.

Sponsors & Collaborators

• Erasmus Medical Center: lead
• Maastro Clinic, The Netherlands: collaborator
• Hospices Civils de Lyon: collaborator

Study Sites (1)

Erasmus MC

Rotterdam, South Holland, 3015 GD, Netherlands

RECRUITING

Related Publications (2)

• 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 BACKGROUND

• 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.

  PMID: 33316754 BACKGROUND

MeSH Terms

Conditions

Thymic epithelial tumor; Thymoma

Condition Hierarchy (Ancestors)

Neoplasms, Complex and Mixed; Neoplasms by Histologic Type; Neoplasms; Thymus Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Lymphatic Diseases; Hemic and Lymphatic Diseases

Central Study Contacts

Anna Salut Esteve Domínguez

CONTACT

0107043491; a.estevedominguez@erasmusmc.nl

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PhD student

Study Record Dates

• First Submitted: March 4, 2024
• First Posted: March 8, 2024
• Study Start: August 1, 2023
• Primary Completion (Estimated): August 1, 2027
• Study Completion (Estimated): August 1, 2027
• Last Updated: March 27, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will not share

Locations

NL (1)