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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 plots of the PC1 and PC2 values of the five sensor NA-NOSE responses of (A) HNC and healthy sub-populations, (B) LC and healthy sub-populations, (C) HNC and LC, and (D) all patients: HNC, LC, and healthy controls. Each patient is represented by one point in plot. The first two principal components depicted contained 80, 67 and 70 and 66% for (A–D), respectively, of the total variance in the data. All test persons including the misclassified were considered in the statistical analysis.
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fig1: PCA plots of the PC1 and PC2 values of the five sensor NA-NOSE responses of (A) HNC and healthy sub-populations, (B) LC and healthy sub-populations, (C) HNC and LC, and (D) all patients: HNC, LC, and healthy controls. Each patient is represented by one point in plot. The first two principal components depicted contained 80, 67 and 70 and 66% for (A–D), respectively, of the total variance in the data. All test persons including the misclassified were considered in the statistical analysis.

Mentions: Figure 1A shows the first two principal components that contain >80% of the variability within the data. A very clear separation between 16 tested HNC patients and 26 healthy subjects can be observed, with no overlap between the clusters for the small study population. As can be seen, the separation is almost entirely along the PC1 axis, with negative values for the healthy and positive values for the HNC states, respectively.


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 plots of the PC1 and PC2 values of the five sensor NA-NOSE responses of (A) HNC and healthy sub-populations, (B) LC and healthy sub-populations, (C) HNC and LC, and (D) all patients: HNC, LC, and healthy controls. Each patient is represented by one point in plot. The first two principal components depicted contained 80, 67 and 70 and 66% for (A–D), respectively, of the total variance in the data. All test persons including the misclassified were considered in the statistical analysis.
© Copyright Policy
Related In: Results  -  Collection

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

fig1: PCA plots of the PC1 and PC2 values of the five sensor NA-NOSE responses of (A) HNC and healthy sub-populations, (B) LC and healthy sub-populations, (C) HNC and LC, and (D) all patients: HNC, LC, and healthy controls. Each patient is represented by one point in plot. The first two principal components depicted contained 80, 67 and 70 and 66% for (A–D), respectively, of the total variance in the data. All test persons including the misclassified were considered in the statistical analysis.
Mentions: Figure 1A shows the first two principal components that contain >80% of the variability within the data. A very clear separation between 16 tested HNC patients and 26 healthy subjects can be observed, with no overlap between the clusters for the small study population. As can be seen, the separation is almost entirely along the PC1 axis, with negative values for the healthy and positive values for the HNC states, respectively.

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