Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors
Ориентир для пациента и семьи
Простыми словами
Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.
- Что изучают
- В протоколе указаны: Artificial Intelligence Diagnostics, Recurrence Prediction Tool.
- Кому может быть актуально
- Состояния в реестре: Thymic Epithelial Tumor, Thymic Carcinoma, Thymoma, Thymoma and Thymic Carcinoma. Базовые параметры: Без ограничений · Все.
- Что важно проверить
- Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
- Где проводится
- Нидерланды
- Следующий шаг
- Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →
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Официальное название
Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors
Обзор
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.
Вмешательства
- Диагностический тест Artificial Intelligence Diagnostics
AI Diagnostics uses advanced algorithms for precise histological image analysis to help diagnose disease, including subtype. - Диагностический тест 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.
Первичные конечные точки
- WP1 - Databases/Data Pre-processing [Срок оценки: M1-M18]
Вторичные конечные точки (1)
- WP2 - Deep Learning-Model for TET Classification and Recurrence Prediction [Срок оценки: M6-M32]
Критерии участия
Критерии включения
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.
Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.
Дизайн исследования
- Модель наблюдения
- Когортное
Центры проведения
Нидерланды · 1 центр
- Erasmus MC — Rotterdam
Публикации
- 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
- 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 PMID 33316754
Идентификаторы
NCT: NCT06301945 · Maastro Clinic · 72725524