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Diagnosis of head-and-neck cancer from exhaled breath.

Hakim M, Billan S, Tisch U, Peng G, Dvrokind I, Marom O, Abdah-Bortnyak R, Kuten A, Haick H - Br. J. Cancer (2011)

Bottom Line: The NA-NOSE signals were analysed to detect statistically significant differences between the sub-populations using (i) principal component analysis with ANOVA and Student's t-test and (ii) support vector machines and cross-validation.The GC-MS analysis showed statistically significant differences in the chemical composition of the breath of the three groups.The presented results could lead to the development of a cost-effective, fast, and reliable method for the differential diagnosis of HNC that is based on breath testing with an NA-NOSE, with a future potential as screening tool.

View Article: PubMed Central - PubMed

Affiliation: The Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 32000, Israel.

ABSTRACT

Background: Head-and-neck cancer (HNC) is the eighth most common malignancy worldwide. It is often diagnosed late due to a lack of screening methods and overall cure is achieved in <50% of patients. Head-and-neck cancer sufferers often develop a second primary tumour that can affect the entire aero-digestive tract, mostly HNC or lung cancer (LC), making lifelong follow-up necessary.

Methods: Alveolar breath was collected from 87 volunteers (HNC and LC patients and healthy controls) in a cross-sectional clinical trial. The discriminative power of a tailor-made Nanoscale Artificial Nose (NA-NOSE) based on an array of five gold nanoparticle sensors was tested, using 62 breath samples. The NA-NOSE signals were analysed to detect statistically significant differences between the sub-populations using (i) principal component analysis with ANOVA and Student's t-test and (ii) support vector machines and cross-validation. The identification of NA-NOSE patterns was supported by comparative analysis of the chemical composition of the breath through gas chromatography in conjunction with mass spectrometry (GC-MS), using 40 breath samples.

Results: The NA-NOSE could clearly distinguish between (i) HNC patients and healthy controls, (ii) LC patients and healthy controls, and (iii) HNC and LC patients. The GC-MS analysis showed statistically significant differences in the chemical composition of the breath of the three groups.

Conclusion: The presented results could lead to the development of a cost-effective, fast, and reliable method for the differential diagnosis of HNC that is based on breath testing with an NA-NOSE, with a future potential as screening tool.

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Related in: MedlinePlus

PCA of the PC1 and PC2 values resulting from statistical analysis of the abundance of volatile biomarkers determined by GC–MS/SPME analysis, using (A) six common volatile biomarkers for distinguishing HNC from healthy states; (B) seven common volatile biomarkers to distinguish HNC from LC. The compound names, masses, and CAS registry numbers are listed in the tables on the right of the PC plots.
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fig2: PCA of the PC1 and PC2 values resulting from statistical analysis of the abundance of volatile biomarkers determined by GC–MS/SPME analysis, using (A) six common volatile biomarkers for distinguishing HNC from healthy states; (B) seven common volatile biomarkers to distinguish HNC from LC. The compound names, masses, and CAS registry numbers are listed in the tables on the right of the PC plots.

Mentions: GC–MS analysis was carried out as supportive method to validate the patterns that stem from response of the NA-NOSE to the breath samples of the HNC and LC patients, and healthy controls. The chemical analysis of the collected exhaled alveolar breath identified several substances that differ in average abundance in breath samples taken from the HNC patients and healthy controls. Hence, it is likely to consider these substances as potential volatile biomarkers of HNC. For this purpose, a representative subset of the collected breath samples were analysed (8 HNC, 15 healthy controls; see Table 1) using GC–MS in combination with SPME. The right panel of Figure 2A lists six common compounds that (i) are present in both >80% of HNC and >80% of healthy subjects and (ii) differ sufficiently in their average abundances in the two sub-populations (no overlap of the 95% CIs represented by 1.96 × s.e.,). Note that the identification of the biomarkers through spectral library match and retention times is tentative, because confirmation of identity through GC–MS analysis of reference substances was carried out only for p-xylene. However, the comparison between patients was based on compound masses and retention times.


Diagnosis of head-and-neck cancer from exhaled breath.

Hakim M, Billan S, Tisch U, Peng G, Dvrokind I, Marom O, Abdah-Bortnyak R, Kuten A, Haick H - Br. J. Cancer (2011)

PCA of the PC1 and PC2 values resulting from statistical analysis of the abundance of volatile biomarkers determined by GC–MS/SPME analysis, using (A) six common volatile biomarkers for distinguishing HNC from healthy states; (B) seven common volatile biomarkers to distinguish HNC from LC. The compound names, masses, and CAS registry numbers are listed in the tables on the right of the PC plots.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3101906&req=5

fig2: PCA of the PC1 and PC2 values resulting from statistical analysis of the abundance of volatile biomarkers determined by GC–MS/SPME analysis, using (A) six common volatile biomarkers for distinguishing HNC from healthy states; (B) seven common volatile biomarkers to distinguish HNC from LC. The compound names, masses, and CAS registry numbers are listed in the tables on the right of the PC plots.
Mentions: GC–MS analysis was carried out as supportive method to validate the patterns that stem from response of the NA-NOSE to the breath samples of the HNC and LC patients, and healthy controls. The chemical analysis of the collected exhaled alveolar breath identified several substances that differ in average abundance in breath samples taken from the HNC patients and healthy controls. Hence, it is likely to consider these substances as potential volatile biomarkers of HNC. For this purpose, a representative subset of the collected breath samples were analysed (8 HNC, 15 healthy controls; see Table 1) using GC–MS in combination with SPME. The right panel of Figure 2A lists six common compounds that (i) are present in both >80% of HNC and >80% of healthy subjects and (ii) differ sufficiently in their average abundances in the two sub-populations (no overlap of the 95% CIs represented by 1.96 × s.e.,). Note that the identification of the biomarkers through spectral library match and retention times is tentative, because confirmation of identity through GC–MS analysis of reference substances was carried out only for p-xylene. However, the comparison between patients was based on compound masses and retention times.

Bottom Line: The NA-NOSE signals were analysed to detect statistically significant differences between the sub-populations using (i) principal component analysis with ANOVA and Student's t-test and (ii) support vector machines and cross-validation.The GC-MS analysis showed statistically significant differences in the chemical composition of the breath of the three groups.The presented results could lead to the development of a cost-effective, fast, and reliable method for the differential diagnosis of HNC that is based on breath testing with an NA-NOSE, with a future potential as screening tool.

View Article: PubMed Central - PubMed

Affiliation: The Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 32000, Israel.

ABSTRACT

Background: Head-and-neck cancer (HNC) is the eighth most common malignancy worldwide. It is often diagnosed late due to a lack of screening methods and overall cure is achieved in <50% of patients. Head-and-neck cancer sufferers often develop a second primary tumour that can affect the entire aero-digestive tract, mostly HNC or lung cancer (LC), making lifelong follow-up necessary.

Methods: Alveolar breath was collected from 87 volunteers (HNC and LC patients and healthy controls) in a cross-sectional clinical trial. The discriminative power of a tailor-made Nanoscale Artificial Nose (NA-NOSE) based on an array of five gold nanoparticle sensors was tested, using 62 breath samples. The NA-NOSE signals were analysed to detect statistically significant differences between the sub-populations using (i) principal component analysis with ANOVA and Student's t-test and (ii) support vector machines and cross-validation. The identification of NA-NOSE patterns was supported by comparative analysis of the chemical composition of the breath through gas chromatography in conjunction with mass spectrometry (GC-MS), using 40 breath samples.

Results: The NA-NOSE could clearly distinguish between (i) HNC patients and healthy controls, (ii) LC patients and healthy controls, and (iii) HNC and LC patients. The GC-MS analysis showed statistically significant differences in the chemical composition of the breath of the three groups.

Conclusion: The presented results could lead to the development of a cost-effective, fast, and reliable method for the differential diagnosis of HNC that is based on breath testing with an NA-NOSE, with a future potential as screening tool.

Show MeSH
Related in: MedlinePlus