Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors (INTHYM)

March 26, 2024 updated by: Anna Salut Esteve Domínguez, Erasmus Medical Center

Artificial Intelligence for Histopathological Classification and Recurrence Prediction of 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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

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.

We 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.

Study Type

Observational

Enrollment (Estimated)

1020

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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
AI Diagnostics uses advanced algorithms for precise histological image analysis to help diagnose disease, including subtype.
Other Names:
  • AI Diagnostics, AI Classification
Recurrence
Patients with thymic epithelial tumors who have experienced recurrence.
Diagnostic test: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
M1-M18

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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).
M6-M32

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
M6-M36

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Erasmus Medical Center

Collaborators

Hospices Civils de Lyon
Maastro Clinic, The Netherlands

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2023

Primary Completion (Estimated)

August 1, 2027

Study Completion (Estimated)

August 1, 2027

Study Registration Dates

First Submitted

March 4, 2024

First Submitted That Met QC Criteria

March 4, 2024

First Posted (Actual)

March 8, 2024

Study Record Updates

Last Update Posted (Actual)

March 27, 2024

Last Update Submitted That Met QC Criteria

March 26, 2024

Last Verified

March 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Maastro Clinic
  • 72725524 (Other Grant/Funding Number: Hanarth Fonds)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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