Clinical trial NCT06301945: Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors
Official title: Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Thymic Epithelial Tumor
Thymic Carcinoma
Thymoma
Thymoma and Thymic Carcinoma
Keywords: Artificial Intelligence
Digital pathology
Lead sponsor: Erasmus Medical Center
Sponsor class: OTHER
Collaborators: name: Maastro Clinic, The Netherlands
name: Hospices Civils de Lyon
First posted: 2024-03-08
Enrollment: 1020
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: WP1 - Databases/Data Pre-processing
timeFrame: M1-M18
Secondary outcomes:
measure: WP2 - Deep Learning-Model for TET Classification and Recurrence Prediction
timeFrame: M6-M32
Other outcomes:
measure: WP3: Clinical Evaluation
timeFrame: M6-M36
Locations:
city: Rotterdam
country: Netherlands
facility: Erasmus MC
state: South Holland
Study officials:
References:
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.
citation: 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.