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出境医 / 临床实验 / Cancer Diagnoses From Exhaled Breath With Na-nose
Cancer Diagnoses From Exhaled Breath With Na-nose
Study Description
Brief Summary:
Early diagnoses of malignant tumors are pivotal for improving their prognoses. The Exhaled Breath is made up of oxygen, carbon dioxide, nitrogen, water, inert gases and volatile organic compounds (VOCs). Theoretically, the concentration of VOCs in exhalation produced by metabolism in human body is only about nmol/L-pmol/L, which can significantly increase under certain pathological conditions. A series of studies of VOCs diagnosing solid tumors the investigators had been conducted in the past decade. It was found that VOCs in exhaled breath can not only distinguish different types of tumors, but also can make a clear distinction between different stages. Our long-term collaborator, Professor Hossam Haick (Israel Institute of Technology) has developed a nano sensor array, so called Na-nose, which can detect VOCs of the exhaled breath by binding gases to specific chemiresistors coated with gold nanomaterials. The Na-nose has the advantages of low cost, easy to use, good reproducibility and real-time detection for large scale clinical application. This study was to use large clinical samples to validate the diagnostic efficacy of the newly developed Nano-nose( Sniffphone and Breath Screener) for malignant tumors .
| Condition or disease | Intervention/treatment |
|---|---|
| Volatile Organic Compounds Cancer Diagnoses Disease | Diagnostic Test: Nanomaterial-based sensors |
Detailed Description:
Israel Institute of Technology provides two type of Na-nose. One is Breath Screener used for large-scale sampling and feature VOCs extraction to establish database. The other is called Sniff Phone aim at clinical real-time VOCs detection assisted by software. About 10,000 patients will participate in the subject of Breath Screener in batches. First, 7000 patients will have a definitive diagnosis and exhaled breath collected. Feature VOCs of specific tumors will be extracted from these samples and employed to build predictive model by using discriminant factor analysis (DFA). After the predictive model had been completed, 3000 definitively diagnosed patients will participate in validating the specificity and sensitivity of the prediction model. With the assistance of Breath Screener clinical database and software services, Sniff Phone is more suitable for clinical real-time detection for its small and convenient design characteristics. At last, Breath Screener and Sniff Phone will continue enriching databases and improve diagnosis efficacy in their clinical applications.
Study Design
| Study Type : | Observational |
| Estimated Enrollment : | 10000 participants |
| Observational Model: | Other |
| Time Perspective: | Prospective |
| Official Title: | Diagnosing Cancers From Healthy From Human Exhaled Breath With Na-nose |
| Estimated Study Start Date : | July 1, 2019 |
| Estimated Primary Completion Date : | December 31, 2020 |
| Estimated Study Completion Date : | December 31, 2022 |
Arms and Interventions
| Group/Cohort | Intervention/treatment |
|---|---|
|
cancer
Patients with definitively diagnosed of solid tumors
|
Diagnostic Test: Nanomaterial-based sensors
Chemical sensors based on Monolayer-Capped Metallic Nanoparticles (MCMNPs) can recognize and classify exhaled breath by special recognition algorithm, which achieves the purpose of disease diagnosis.
|
|
Benign disease
Patients with definitively diagnosed of benign disease or precancerous lesion
|
Diagnostic Test: Nanomaterial-based sensors
Chemical sensors based on Monolayer-Capped Metallic Nanoparticles (MCMNPs) can recognize and classify exhaled breath by special recognition algorithm, which achieves the purpose of disease diagnosis.
|
|
Normal
Healthy volunteers
|
Diagnostic Test: Nanomaterial-based sensors
Chemical sensors based on Monolayer-Capped Metallic Nanoparticles (MCMNPs) can recognize and classify exhaled breath by special recognition algorithm, which achieves the purpose of disease diagnosis.
|
Outcome Measures
Primary Outcome Measures :
- Build predictive diagnosis database [ Time Frame: From July 01,2019 to December 31,2021 ]First, feature VOCs of specific tumors will be extracted from part of collected samples and employed to build predictive model. After the predictive model had been completed, number of definitively diagnosed patients will participate in validating the specificity and sensitivity of the prediction model.
Secondary Outcome Measures :
- Associated feature exhaled breath with differentially expressed genes [ Time Frame: From Juan 01,2022 to December 31,2022 ]Integrate the correlation and relevance between the exhaled samples and the differentially expressed genes in the cancer group and the benign / normal control group to explore the mechanism of feature VOCs' production.
Eligibility Criteria
| Ages Eligible for Study: | 18 Years to 75 Years (Adult, Older Adult) |
| Sexes Eligible for Study: | All |
| Accepts Healthy Volunteers: | Yes |
| Sampling Method: | Non-Probability Sample |
Study Population
10,000 volunteers who had a definitively diagnosis with surgery or endoscope
Criteria
Inclusion Criteria:
- 18-75 years
- Cancer/benign disease having been diagnosed by pathology
- ECOG < 2
Exclusion Criteria:
- Concomitant malignancies other than one malignant tumor
- Diabetes, Fatty liver
- Autoimmune disease
- Ventilation and transaired function obstacle
Contacts and Locations
Contacts
| Contact: Bao Chuyang, MD | +86 18555039598 | des_mond@outlook.com | |
| Contact: Hu Liu, MD | +86 13866175691 | drliuhu@yahoo.com |
Sponsors and Collaborators
Anhui Medical University
Technion, Israel Institute of Technology
Investigators
| Principal Investigator: | Hu Liu, MD | Anhui Provincial Hospital |
| Tracking Information | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| First Submitted Date | May 27, 2019 | ||||||||
| First Posted Date | May 30, 2019 | ||||||||
| Last Update Posted Date | May 31, 2019 | ||||||||
| Estimated Study Start Date | July 1, 2019 | ||||||||
| Estimated Primary Completion Date | December 31, 2020 (Final data collection date for primary outcome measure) | ||||||||
| Current Primary Outcome Measures |
Build predictive diagnosis database [ Time Frame: From July 01,2019 to December 31,2021 ] First, feature VOCs of specific tumors will be extracted from part of collected samples and employed to build predictive model. After the predictive model had been completed, number of definitively diagnosed patients will participate in validating the specificity and sensitivity of the prediction model.
|
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| Original Primary Outcome Measures | Same as current | ||||||||
| Change History | |||||||||
| Current Secondary Outcome Measures |
Associated feature exhaled breath with differentially expressed genes [ Time Frame: From Juan 01,2022 to December 31,2022 ] Integrate the correlation and relevance between the exhaled samples and the differentially expressed genes in the cancer group and the benign / normal control group to explore the mechanism of feature VOCs' production.
|
||||||||
| Original Secondary Outcome Measures | Same as current | ||||||||
| Current Other Pre-specified Outcome Measures | Not Provided | ||||||||
| Original Other Pre-specified Outcome Measures | Not Provided | ||||||||
| Descriptive Information | |||||||||
| Brief Title | Cancer Diagnoses From Exhaled Breath With Na-nose | ||||||||
| Official Title | Diagnosing Cancers From Healthy From Human Exhaled Breath With Na-nose | ||||||||
| Brief Summary | Early diagnoses of malignant tumors are pivotal for improving their prognoses. The Exhaled Breath is made up of oxygen, carbon dioxide, nitrogen, water, inert gases and volatile organic compounds (VOCs). Theoretically, the concentration of VOCs in exhalation produced by metabolism in human body is only about nmol/L-pmol/L, which can significantly increase under certain pathological conditions. A series of studies of VOCs diagnosing solid tumors the investigators had been conducted in the past decade. It was found that VOCs in exhaled breath can not only distinguish different types of tumors, but also can make a clear distinction between different stages. Our long-term collaborator, Professor Hossam Haick (Israel Institute of Technology) has developed a nano sensor array, so called Na-nose, which can detect VOCs of the exhaled breath by binding gases to specific chemiresistors coated with gold nanomaterials. The Na-nose has the advantages of low cost, easy to use, good reproducibility and real-time detection for large scale clinical application. This study was to use large clinical samples to validate the diagnostic efficacy of the newly developed Nano-nose( Sniffphone and Breath Screener) for malignant tumors . | ||||||||
| Detailed Description | Israel Institute of Technology provides two type of Na-nose. One is Breath Screener used for large-scale sampling and feature VOCs extraction to establish database. The other is called Sniff Phone aim at clinical real-time VOCs detection assisted by software. About 10,000 patients will participate in the subject of Breath Screener in batches. First, 7000 patients will have a definitive diagnosis and exhaled breath collected. Feature VOCs of specific tumors will be extracted from these samples and employed to build predictive model by using discriminant factor analysis (DFA). After the predictive model had been completed, 3000 definitively diagnosed patients will participate in validating the specificity and sensitivity of the prediction model. With the assistance of Breath Screener clinical database and software services, Sniff Phone is more suitable for clinical real-time detection for its small and convenient design characteristics. At last, Breath Screener and Sniff Phone will continue enriching databases and improve diagnosis efficacy in their clinical applications. | ||||||||
| Study Type | Observational | ||||||||
| Study Design | Observational Model: Other Time Perspective: Prospective |
||||||||
| Target Follow-Up Duration | Not Provided | ||||||||
| Biospecimen | Not Provided | ||||||||
| Sampling Method | Non-Probability Sample | ||||||||
| Study Population | 10,000 volunteers who had a definitively diagnosis with surgery or endoscope | ||||||||
| Condition |
|
||||||||
| Intervention | Diagnostic Test: Nanomaterial-based sensors
Chemical sensors based on Monolayer-Capped Metallic Nanoparticles (MCMNPs) can recognize and classify exhaled breath by special recognition algorithm, which achieves the purpose of disease diagnosis.
|
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| Study Groups/Cohorts |
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| Publications * |
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* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline. |
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| Recruitment Information | |||||||||
| Recruitment Status | Not yet recruiting | ||||||||
| Estimated Enrollment |
10000 | ||||||||
| Original Estimated Enrollment | Same as current | ||||||||
| Estimated Study Completion Date | December 31, 2022 | ||||||||
| Estimated Primary Completion Date | December 31, 2020 (Final data collection date for primary outcome measure) | ||||||||
| Eligibility Criteria |
Inclusion Criteria:
Exclusion Criteria:
|
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| Sex/Gender |
|
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| Ages | 18 Years to 75 Years (Adult, Older Adult) | ||||||||
| Accepts Healthy Volunteers | Yes | ||||||||
| Contacts |
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| Listed Location Countries | Not Provided | ||||||||
| Removed Location Countries | |||||||||
| Administrative Information | |||||||||
| NCT Number | NCT03967652 | ||||||||
| Other Study ID Numbers | NanoBreathDiag | ||||||||
| Has Data Monitoring Committee | Not Provided | ||||||||
| U.S. FDA-regulated Product |
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| IPD Sharing Statement |
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| Responsible Party | Hu Liu, Anhui Provincial Hospital | ||||||||
| Study Sponsor | Anhui Medical University | ||||||||
| Collaborators | Technion, Israel Institute of Technology | ||||||||
| Investigators |
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| PRS Account | Anhui Medical University | ||||||||
| Verification Date | May 2019 | ||||||||
