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Идёт набор NCT07392567

Prospective Validation of an AI Model for Predicting Liver Metastasis in Colorectal Cancer

Наблюдательное Colorectal Cancer Liver Metastasis

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: Multimodal Deep Learning Prediction Model.
Кому может быть актуально
Состояния в реестре: Colorectal Cancer Liver Metastasis. Базовые параметры: 18 лет — 75 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
Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →
Официальное название

A Multicenter, Prospective, Observational Study for the Validation of a Multimodal Deep Learning Model to Predict Metachronous Liver Metastasis in Patients With Colorectal Cancer After Curative Resection

Обзор

This is a prospective, multicenter, observational study designed to validate the predictive accuracy of a pre-developed multimodal deep learning model. The model integrates preoperative contrast-enhanced CT scans, digitized postoperative pathology images, and standard clinical data to estimate the risk of liver metastasis within two years after curative surgery in patients with stage I-III colorectal cancer. The primary objective is to evaluate the model's performance in an independent, prospectively enrolled patient cohort. Participants will receive standard-of-care treatment according to clinical guidelines. The study involves no experimental interventions; it solely involves the collection and analysis of routinely generated clinical data. The goal is to assess the model's potential for clinical translation by providing a reliable tool for stratifying patients' risk of liver metastasis, which could inform personalized surveillance strategies.

Вмешательства

  • Диагностический тест Multimodal Deep Learning Prediction Model
    This is a non-therapeutic, prognostic study. The intervention under investigation is the application of a pre-specified multimodal deep learning model that integrates preoperative CT imaging, digital pathology, and clinical data to stratify patients' risk of developing metachronous liver metastasis. This model functions as a prognostic tool and is not used to guide patient management in this study. Its performance is being evaluated prospectively against the actual clinical outcomes.

Первичные конечные точки

  • Area Under the Receiver Operating Characteristic Curve (AUC) [Срок оценки: 2 years after surgery]
Вторичные конечные точки (1)
  • Liver Metastasis-Free Survival (LMFS) by Risk Group [Срок оценки: From the date of surgery until the date of first documented liver metastasis or last follow-up, assessed up to 3 years.]

Критерии участия

Критерии включения

  • Age 18-75 years, any gender.
  • Clinical diagnosis of primary colon or rectal adenocarcinoma (Stage I-III). Scheduled to undergo curative radical resection for colorectal cancer.
  • Preoperative contrast-enhanced abdominal/pelvic CT scan performed within 1 month before surgery, with acceptable image quality.
  • No evidence of distant metastasis (including synchronous liver metastasis) on preoperative examination.
  • ECOG Performance Status of 0 or 1.
  • Patient or their legal representative voluntarily participates and provides written informed consent.

Критерии исключения

  • Postoperative pathological confirmation of non-primary colorectal adenocarcinoma or presence of distant metastasis.
  • Intraoperative determination of non-R0 resection, or performance of palliative surgery/ostomy only.
  • History of other malignant tumors.
  • Previous history of liver surgery or liver transplantation.
  • Death within the perioperative period (within 30 days after surgery).
  • Refusal to participate in follow-up, withdrawal of informed consent, or loss to follow-up.

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 · 1 центр
  • Tongji Hospital — Ухань

Идентификаторы

NCT: NCT07392567 · TJ-IRB202601017

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗