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NCT Number: NCT06301945

Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors

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.

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Key information

Conditions

Thymic Epithelial Tumor Hemic and Lymphatic Diseases Lymphatic Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Complex and Mixed Thoracic Neoplasms Thymic Carcinoma Thymoma Thymoma and Thymic Carcinoma Thymus Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

Erasmus MC

Rotterdam, South Holland, 3015 GD, Netherlands

Location status: Recruiting

Location contact

Anna Salut Esteve Domínguez

CONTACT

[email protected]

Who can participate

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Artificial Intelligence Diagnostics

Diagnostic Test

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

Other names: AI Diagnostics, AI Classification

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.

Primary outcomes

  1. WP1 - Databases/Data Pre-processing

    Time frame: 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 outcomes

  1. WP2 - Deep Learning-Model for TET Classification and Recurrence Prediction

    Time frame: 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).

Other outcomes

  1. WP3: Clinical Evaluation

    Time frame: M6-M36

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Anna Salut Esteve Domínguez

CONTACT

[email protected]

0107043491

Sponsors and collaborators

Lead sponsor

Erasmus Medical Center

Other

Collaborators

  • Hospices Civils de Lyon
  • Maastro Clinic, The Netherlands

Registry information

Official study title

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

Acronym: INTHYM

Important dates

Study start
2023
Primary completion
2027
Study completion
2027
First posted
Mar 8, 2024
Registry last updated
Mar 27, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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