Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors

ConditionsThymic Epithelial Tumor Thymic Carcinoma Thymoma Thymoma and Thymic Carcinoma

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

AgeNot listed

SponsorErasmus Medical Center

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Thymoma A

Thymoma AB

Thymoma B1

Thymoma B2

Disqualifiers

None

Trial 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.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

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

Treatment groups

1,020 Participants

are divided into 2 treatment groups

Group A: Patients with TET1 intervention

Group B: Recurrence1 intervention

Trial outcomes

Primary outcomes

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.

Time frame

M1-M18

Secondary outcomes

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).

Time frame

M6-M32

Other outcomes

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.

Time frame

M6-M36

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Erasmus Medical Center

Lead sponsor

Maastro Clinic, The Netherlands

Collaborator

Hospices Civils de Lyon

Collaborator

Source ClinicalTrials.gov

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