Cancer-related Volatile Organic Compounds Research Articles

A colorimetric detector for lung cancer related volatile organic compounds based on cross-response mechanism

In this paper, a detection device for lung cancer related Volatile Organic Compounds (VOCs) was constructed. A sensor array based on a cross-responsive mechanism, using Porphyrin and its derivates, was designed. A new uniformly-illuminative light source was made to insure the RGB signals accurately and stably acquired by CMOS camera. And Trace-pro software was used to simulate the performance of the light source. The data collection and processing system was used to detect color changes of the sensor arrays before and after reaction, which extract unique patterns of the tested VOCs. Four selected VOCs, p-xylene, styrene, isoprene and hexanal, were detected by the proposed device. Unsupervised pattern recognition methods, hierarchical cluster analysis (HCA) and principal component analysis (PCA), were used to analyze the data. The results showed that the methods could 100% discriminate the four VOCs and the device obtained responses at concentrations varying from 50ppb to 500ppb. In conclusion, the proposed detection device showed excellent selectivity and discrimination ability for the VOCs related to lung cancer. Furthermore, our preliminary study demonstrated that the proposed detection device has brilliant potential application for early clinical diagnosis of lung cancer.

A novel device based on a fluorescent cross-responsive sensor array for detecting lung cancer related volatile organic compounds.

In this paper, a novel, simple, rapid, and low-cost detection device for lung cancer related Volatile Organic Compounds (VOCs) was constructed. For this task, a sensor array based on cross-responsive mechanism was designed. A special gas chamber was made to insure sensor array exposed to VOCs sufficiently and evenly, and FLUENT software was used to simulate the performance of the gas chamber. The data collection and processing system was used to detect fluorescent changes of the sensor arrays before and after reaction, and to extract unique patterns of the tested VOCs. Four selected VOCs, p-xylene, styrene, isoprene, and hexanal, were detected by the proposed device. Unsupervised pattern recognition methods, hierarchical cluster analysis and principal component analysis, were used to analyze data. The results showed that the methods could 100% discriminate the four VOCs. What is more, combined with artificial neural network, the correct rate of quantitative detection was up to 100%, and the device obtained responses at concentrations below 50 ppb. In conclusion, the proposed detection device showed excellent selectivity and discrimination ability for the VOCs related to lung cancer. Furthermore, our preliminary study demonstrated that the proposed detection device has brilliant potential application for early clinical diagnosis of lung cancer.

Discrimination of Lung Cancer Related Volatile Organic Compounds with a Colorimetric Sensor Array

A simple and rapid discrimination of four lung cancer related volatile organic compounds (VOCs) was achieved with a novel colorimetric sensor array. Based on the cross-responsive mechanism, the sensor system exhibited high sensitivity to selected lung cancer biomarkers, including p-xylene, styrene, isoprene, and hexanal with concentrations varying from 50 ppb to 500 ppb. By extracting color information, it provided good selectivity and fine discrimination of selected gases via pattern recognition with Fisher linear discriminant (FLD). Additionally, with the employment of the Takagi-Sogeno Fuzzy Neural Network (TSFNN), different concentrations of selected VOCs were discriminated. It also suggested that the colorimetric sensor array proposed could not only distinguish different lung cancer related VOCs but also discriminate specific VOCs of different concentrations with an average rate of classification up to 95%. Our preliminary study demonstrated that the cross-responsive sensor array had infinite potential for further clinical and commercial use for early diagnosis of lung cancer.

Abstract A36: Breath Biomarkers in the Post NLST-era for the Discrimination between Malignant from Benign Pulmonary Nodules

Abstract Background: The recent National Lung Cancer Screening Trial (NLST) has proven that screening for lung cancer by low dose CT (LDCT) scans reduces the related mortality rate by 20%. However, in this program, the false positive rate was extremely high: 96% out of the 24% positive CT findings were non-cancerous. A high rate of false positives leads to unnecessary invasive procedures, which are both costly and associated with significant morbidity and mortality. It is now widely anticipated that LDCT screening programs will be launched in many countries in the near future. This will lead to a dramatically increased detection of small solitary pulmonary nodules (SPNs) for invasive investigation. Consequently, additional non-invasive biomarker approach to distinguish between benign and cancerous conditions is necessary. For that purpose, we evaluated in this study the role of exhale breath analysis as a potential non-invasive biomarker to discriminate between benign and malignant SPNs in the post NLST-era. Objectives: Developing a signature of volatile organic compounds (VOCs) in the exhaled breath that aids to distinguishes between benign and cancerous SPNs, and therefore also aids in 1) Early detection of lung cancer, 2) Improves the specificity of the NLST screening protocol in high risk cohorts, 3) helps prevent unnecessary invasive procedures, 4) results in the early treatment of lung cancer, and 5) Improves the costeffectiveness of the NLST screening program. Specifically, in this study we report the VOCs signature that discriminates benign from malignant SPNs, between NSCLC and SCLC and between early and advanced NSCLC. Methods: Cross-sectional comparative survey from 74 patients with solitary pulmonary nodules (SPNs) attending the University of Colorado Cancer Center or Denver Veterans Affairs Medical Center; USA. Breath samples were taken and the VOC profiles for malignant and benign lung nodules were determined by gas-chromatography/mass-spectrometry (GCMS), and the corresponding collective VOCs patterns were identified by a nanomaterialbased array of sensors. Results: Among the 74 high risk patients with SPNs on their LDCTs, 53 were malignant and 21 were benign. Age, smoking history, co-morbidity and medications were similar in both groups. Nodule sizes were 2.7±1.7 cm vs. 1.9±1.1 cm accordingly (NS). Within the malignant group, 47 were NSCLC and 6 were SCLC. Thirty had early disease (stage I-II/limited) and 23 had advanced disease (stage III-IV/extensive). GC-MS analysis identified two VOCs in the exhaled breath of nodule positive patients that showed statistically significant differences in concentration for benign and malignant lung nodules (N=11, 28 respectively; Benzene, 1-methyl-4-(1-methylethyl) and 1-Octene). The sensor array could distinguish between the corresponding collective VOCs patterns with an accuracy of 89.4%. Among the malignant SPNs we could further distinguish between SCLC and NSCLC (accuracy 93.9%) and between early and advanced disease (89.7%). Conclusions: An array of nanomaterial-based sensors could discriminate significantly between benign and malignant SPNs in a high-risk cohort with positive LDCTs based on lung cancer related VOCs profiles. Further, it was able to discriminate between NSCLC and SCLC, as well as between early and advanced disease. These results could promote the development of a non-invasive, fast and potentially cost-effective diagnostic tool for the management of nodule-positive patients that could help to eliminate risky invasive procedures in patients with benign nodules in the post NLST-era.