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Набор скоро начнётся NCT07685028

LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol A

Без фазы С лечением Family History of Lung Cancer

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: CT scan, Sybil.
Кому может быть актуально
Состояния в реестре: Family History of Lung Cancer. Базовые параметры: 18 лет — 80 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
США
Следующий шаг
Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →

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Обзор

This research is being done to determine if an image-based deep learning model (Sybil) can accurately predict the likelihood of future lung cancer based on chest computed tomography (CT) imaging from individuals.

Подробное описание

This non-therapeutic study will enroll individuals who have family history of lung cancer. Participants will undergo a low-dose non-contrast computed tomography of the chest (LDCT) and may also send images from any chest CT scan(s) obtained as part of routine clinical care, outside of the study. The images and data collected will be analyzed by an image-based deep learning model (Sybil). Sybil is a type of artificial intelligence model that has been shown to accurately predict individuals' future risk of lung cancer based solely on images from a CT Chest scan, but it remains unclear whether Sybil works well in people with a family history of lung cancer. The goals of this study are: 1) to obtain CT Chest images from individuals with a family history of lung cancer in order to test whether Sybil continues to work well, and 2) offer free screening CT scans to qualifying individuals. It is expected that 250 people will take part in this research study.

Вмешательства

  • Диагностический тест CT scan
    Computed tomography scan
  • Другое Sybil
    Image-based deep learning model

Первичные конечные точки

  • Sybil's performance in predicting future lung cancer diagnoses [Срок оценки: Annually, from time of initial CT scan to up to 5 years after the scan.]
Вторичные конечные точки (5)
  • Compare the distribution of Sybil lung cancer risk scores in this trial to the distribution of Sybil risk scores from the NLST clinical trial [Срок оценки: Initial provided CT scan will represent time 0. Additional provided CT scans will vary between individuals and will be measured in years relative to time 0 (e.g., time -3.5 years, time +2 years, etc). Sybil risk scores will be calculated for each scan.]
  • Incidence and prevalence of lung cancer in the study population [Срок оценки: Annually, from time of initial CT scan to up to 5 years after the scan.]
  • Incidence of lung nodules in this population [Срок оценки: Annually, from time of initial CT scan to up to 5 years after the scan.]
  • Prevalence of lung nodules in this population [Срок оценки: Annually, from time of initial CT scan to up to 5 years after the scan.]
  • Describe the characteristics of lung nodules in this population [Срок оценки: At time of each provided CT scan to up to 5 years after the scan.]

Критерии участия

Критерии включения

  • Age: Must meet both the upper and lower age limit criteria.
  • Upper age limit: ≤80 years of age
  • Lower age limit:
  • ≥40 years of age OR
  • ≥18 years of age AND ≤10 years of youngest relative's age at time of lung cancer diagnosis (e.g., if a relative was diagnosed at 35 years of age, participant can enroll at ≥25 years of age)
  • Positive family history of lung cancer (defined as):
  • Has ≥1 first-degree relative, OR
  • Has ≥2 second-degree relatives with a diagnosis of non-small cell lung cancer or small cell lung cancer (NB: a first-degree relative = parent, sibling, or child, a second-degree relative = grandparent, blood-related aunt or uncle, grandchild, blood-related niece or nephew, half-sibling)

Критерии исключения

  • Must not have a personal history of lung cancer at the time of enrollment.
  • Must not have a personal history of stage IV cancer of any type at the time of enrollment.
  • Must not have had surgical removal of any portion of the lung, excluding needle or core lung biopsy at the time of enrollment.
  • Must not have had a chest CT within 12 months prior to trial enrollment.

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

Здоровые добровольцы: Нет

Дизайн исследования

Распределение
Не применимо
Модель
Одна группа
Маскирование
Открытое
Основная цель
Скрининг

Центры проведения

США · 1 центр
  • Massachusetts General Hospital — Boston

Идентификаторы

NCT: NCT07685028 · 26-044

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗