Differentiation of Benign and Malignant Pulmonary Nodules by Volatile Organic Compounds in Human Exhaled Breath

Официальное название

Exploratory Study on the Identification of Benign and Malignant Pulmonary Nodules Using Volatile Organic Compounds in Human Exhaled Breath

Обзор

The goal of this observational study is to develop an advanced expiratory algorithm model utilizing exhaled breath volatile organic compound (VOC) markers. This model aims to accurately differentiate benign from malignant nodules in individuals harboring pulmonary nodules. The primary objectives it strives to accomplish are: 1. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in distinguishing benign and malignant pulmonary nodules. 2. To evaluate the diagnostic effectiveness of an AI model that employs exhaled breath VOC biomakers to identify specific types of malignant nodules, including lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer. 3. To explore and identify key characteristic VOCs combinations that are associated with EGFR site mutations in malignant nodules, further modeling and evaluating the classification performance. By utilizing this comprehensive approach, the study hopes to contribute significantly to early detection and accurate classification of pulmonary nodules, ultimately leading to improved patient care and treatment outcomes.

Подробное описание

This is a prospective, cross-sectional, and observational cohort study aiming at recruiting 3000 participants with pulmonary nodules ranging from 5 to 30 mm in diameter. Prior to invasive surgery, exhaled breath samples will be collected from these participants and analyzed using Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system. Following the acquisition of μGC-PID results, a comprehensive evaluation of the diagnostic performance of VOC biomakers distinguishing between benign and malignant pulmonary nodules will be conducted, leveraging histopathological findings, CT examination data, and clinical data.

Вмешательства

• Другое Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
  Detection of volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID

Первичные конечные точки

• The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in distinguishing benign and malignant pulmonary nodules. [Срок оценки: 3 years]

Вторичные конечные точки (1)

• The diagnostic effectiveness of an AI model to identify specific types of malignant nodules, including lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer. [Срок оценки: 3 years]

Критерии участия

Критерии включения

• 18-80 years old;
• Pulmonary nodules were detected through low-dose spiral CT, chest CT conventional scan, or high-resolution thin-layer CT examination, with a maximum diameter of 5-30 mm, including solid nodules and ground glass nodules;
• Patients require pulmonary nodule resection to define the type of nodule pathology;
• The Patients have not yet used any drugs for tumor treatment;
• Patients and/or family members are able to understand the research protocol and are willing to participate in this study, providing written informed consent.

Критерии исключения

• The maximum diameter of pulmonary nodules is greater than 30 mm;
• Patients are unable to determine the pathological diagnosis of pulmonary nodules after surgical resection or biopsy;
• Patients with recurrent lung cancer;
• Patients who have undergone lung transplantation or lobectomy;
• Individuals who currently or have a history of malignant tumors;
• Patients in the acute phase of inflammation or in need of intensive care in the above selected disease groups;
• Individuals with severe liver and kidney dysfunction;
• Mental illness patients (such as severe dementia, schizophrenia, severe depression, manic depressive psychosis, etc.);
• Confirmed HIV patients;
• Pregnant or lactating women;
• Patients or family members are unable to understand the conditions and objectives of this study.
• The patient is unwilling or unable to personally sign the informed consent form.

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

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 15 центров

• Peking Union Medical College Hospital — Пекин
• First People's Hospital of Foshan — Foshan
• The First Affiliated Hospital of Guangzhou Medical University — Гуанчжоу
• Liwan District Central Hospital — Гуанчжоу
• Guangzhou Development Zone Hospital — Гуанчжоу
• Huangpu District Chinese Medicine Hospital — Гуанчжоу
• Huangpu District Hongshan Street Community Health Service Center — Гуанчжоу
• Huangpu District Jiufo Street Community Health Service Center — Гуанчжоу
• … и ещё 7 центров

Публикации

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Идентификаторы

NCT: NCT06518655 · LCLN01

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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