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Analysis of Breath Volatile Organic Compounds Using Mass Spectrometry

2026年7月23日 更新者：University of Oklahoma

Breath Volatile Organic Compounds (VOC) Analysis Using Proton Transfer Reaction Mass Spectrometry (PTR-MS)

The purpose of this clinical trial is to evaluate whether volatile organic compound (VOC) signatures detected in the breath of patients with cancer can serve as a potential screening tool for the early detection of cancer.

研究概览

地位

尚未招聘

条件

干预/治疗

详细说明

This study aims to determine whether metabolic changes associated with cancer produce distinct alterations in exhaled breath compared with those of healthy individuals. Breath samples will be analyzed using machine learning techniques to identify volatile organic compound (VOC) patterns and develop diagnostic algorithms capable of detecting multiple types of cancer. The long-term goal is to establish a noninvasive, breath-based screening tool that can facilitate the early detection of various cancers.

Additionally, patients and healthy participants who consent to this study may opt in to be contacted in the future to provide additional breath samples.

研究类型

观察性的

注册 (估计的)

2000

联系人和位置

本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。

学习联系方式

研究联系人备份

学习地点

  • 美国
    • Oklahoma
      • Oklahoma City、Oklahoma、美国、73117

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 成人
  • 年长者

接受健康志愿者

是的

取样方法

非概率样本

研究人群

Patients diagnosed with malignancies and healthy individuals who meet the inclusion criteria.

描述

Inclusion Criteria:

  • Age ≥ 18 at the time of consent. Male and female patients to be tested.
  • Capable of understanding written and/or spoken English language.
  • Able to provide informed consent.
  • Cancer of any type.
  • Newly diagnosed cancer and untreated or established diagnosis of cancer. For established cancer patients, no active anti-cancer treatment for more than one month (reasons for no treatment are such as relapse, progression of cancer, refractory, or intolerance to treatment etc.)

Exclusion Criteria:

  • Under the age of 18.
  • Anticipated inability to complete breath sampling procedure.
  • Unable to provide informed consent.
  • Pregnant women
  • Active respiratory infection symptoms
  • Recent use of antibiotics
  • Difficulty in performing coached exhalation
  • Individuals who are unable to follow the instructions
  • Cancer patients who are on active treatment for cancer or have received cancer treatment within one month

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

队列和干预

团体/队列
干预/治疗
Diagnosed Malignancies
Participants in this group either have a newly diagnosed, untreated cancer or a pre-existing cancer diagnosis but are not currently receiving anticancer therapy. Participants with a pre-existing diagnosis must not have received any anticancer treatment within the previous month.
设备：Proton Transfer Reaction Mass Spectrometry Analysis
This is a noninvasive intervention. Participants will be asked to provide a breath sample using a disposable mouthpiece equipped with a saliva/moisture trap and a non-rebreathing valve. Breath samples will be collected through normal, steady exhalation. The entire breath collection process is expected to take no more than 30 minutes to complete.
其他名称：
  • PTR-MS
Healthy Control Group
Participants in this control group have no current or prior diagnosis of a malignancy.
设备：Proton Transfer Reaction Mass Spectrometry Analysis
This is a noninvasive intervention. Participants will be asked to provide a breath sample using a disposable mouthpiece equipped with a saliva/moisture trap and a non-rebreathing valve. Breath samples will be collected through normal, steady exhalation. The entire breath collection process is expected to take no more than 30 minutes to complete.
其他名称：
  • PTR-MS

研究衡量的是什么？

主要结果指标

结果测量
措施说明
大体时间
VOC Signature Collection.
大体时间：2 Years
The successful collection of breath samples from 1000 cancer patients and 1000 healthy volunteers.
2 Years
Assess the sensitivity of Machine Learning (ML) Algorithm In The Test Dataset.
大体时间：1 Years
Using the training dataset, qualitative output generated by the PTR-MS instrument will be analyzed using machine learning methods to identify volatile organic compound (VOC) patterns associated with different cancer types, including pancreatic, esophageal, hepatocellular carcinoma, lung, and ovarian cancers. The trained machine learning model will be tested using the dataset.
1 Years

次要结果测量

结果测量
措施说明
大体时间
Assess The Specificity and Accuracy of ML Analysis In The Test Dataset.
大体时间：1 year
To determine the specificity, and overall diagnostic accuracy of the machine learning (ML) algorithm for detecting pancreatic, esophageal, hepatocellular carcinoma, lung, and ovarian cancers within the test dataset.
1 year

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

University of Oklahoma

调查人员

  • 首席研究员：Nirmal Choradia, MD、University of Oklahoma - Stephenson Cancer Center

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (估计的)

2026年10月1日

初级完成 (估计的)

2027年10月1日

研究完成 (估计的)

2029年10月1日

研究注册日期

首次提交

2026年7月23日

首先提交符合 QC 标准的

2026年7月23日

首次发布 (实际的)

2026年7月29日

研究记录更新

最后更新发布 (实际的)

2026年7月29日

上次提交的符合 QC 标准的更新

2026年7月23日

最后验证

2026年7月1日

更多信息

与本研究相关的术语

关键字

其他相关的 MeSH 术语

其他研究编号

  • OU-SCC-VOC

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

不

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.

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