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Идёт набор NCT07463872

Management of Pancreatic Cystic Lesions Using Artificial Intelligence Based on EUS and Multimodal Data

— Наблюдательное Pancreatic Cystic Lesion Mucinous Cystadenoma of Pancreas Intraductal Papillary Mucinous Neoplasm of Pancreas Pseudocyst Pancreas

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Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
В протоколе указаны: Cyst-AI model.
Кому может быть актуально
Состояния в реестре: Pancreatic Cystic Lesion, Mucinous Cystadenoma of Pancreas, Intraductal Papillary Mucinous Neoplasm of Pancreas, Pseudocyst Pancreas. Базовые параметры: от 18 лет · 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
Китай
Следующий шаг
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Официальное название

A Multimodal Artificial Intelligence Model for Subtyping Diagnosis and Clinical Management of Pancreatic Cystic Lesions Based on Endoscopic Ultrasound and Clinical Information

Обзор

The primary objective is to construct a multimodal AI model (Cyst-AI) based on EUS images and clinical data such as imaging features(CT or MRI) and laboratory tests to assist endoscopists in the diagnosis of pancreatic cystic lesions(PCLs), mainly differentiating mucinous from non-mucinous lesions. The secondary objective is to evaluate the model's effectiveness in risk stratification and clinical management for patients with PCLs.

Подробное описание

With the development of medical imaging technology, the detection rate of pancreatic cystic lesions (PCLs) has been increasing notably. Although most cysts are benign, a considerable subset has the potential for malignant transformation. Clinical management is based on diagnosis and risk stratification. For PCLs，different diagnosis and risk stratification lead to entirely different clinical strategies and outcomes, which are closely related to the quality of life, economic burden, and psychological stress of patients. Endoscopic ultrasound (EUS) has played a crucial role in the further differential diagnosis of PCLs. Artificial intelligence (AI) has also shown great potential in clinical diagnosis and management. Thus, we plan to retrospectively collect patients' EUS imaging data, radiological and laboratory tests, and other clinical information to construct a model named Cyst-AI which integrates the function of diagnosis and clinical management, to assist in clinical decision-making.

Вмешательства

  • Диагностический тест Cyst-AI model
    The multi-center collected data will be divided into a training set, a validation set, and a test set for developing and testing the cyst-AI model.

Первичные конечные точки

  • The performance of the diagnostic model in differentiating mucinous from non-mucinous PCLs [Срок оценки: Within 3 months upon completion of the diagnostic model training.]
  • The risk stratification performance of the clinical management model for mucinous PCLs [Срок оценки: Within 3 months upon completion of the risk stratification model training.]
Вторичные конечные точки (4)
  • The performance of the diagnostic model in differentiating specific types of PCLs [Срок оценки: Within 3 months upon completion of the diagnostic model training.]
  • The clinical management performance of the clinical management model for mucinous PCLs [Срок оценки: Within 3 months upon completion of the clinical management model training.]
  • The performance of the model in assisting endoscopists of different levels in diagnosing and managing PCLs [Срок оценки: Within 1 months upon completion of the human-machine confrontational crossover study]
  • The impact of the model on the decision-making process of endoscopists [Срок оценки: Within 1 months upon completion of the human-machine confrontational crossover study.]

Критерии участия

Критерии включения

  • Patients whose EUS results indicates pancreatic cystic or cystoid lesions;
  • Mucinous lesions: including mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN);
  • Non-mucinous lesions: including pancreatic pseudocyst, serous cystic neoplasm (SCN), cystic neuroendocrine tumor (cNET).

Критерии исключения

  • Patients whose age is less than 18 years old;
  • Patients who have undergone pancreatic surgery before the EUS examination;
  • Patients who have received chemotherapy and radiotherapy for pancreatic tumors before the EUS examination;
  • Pathological results indicate that pancreatic lesions are metastatic lesions from other sites;
  • Patients whose EUS images or reports are missing;
  • EUS image quality does not meet the requirements for review, such as blurry imaging or containing artifacts, biopsy needles, measuring scales, or other additional annotations that are not part of the original EUS image;
  • Patients whose final diagnosis is unclear.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения
Когортное

Центры проведения

Китай · 2 центра
  • Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology — Ухань
  • Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology — Ухань

Идентификаторы

NCT: NCT07463872 · Cyst-AI 2024

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗