Exhaled volatile organic compounds for diagnosis of hepatocellular carcinoma

Abstract

Volatile organic compounds (VOCs) profile for diagnosis and monitoring therapeutic response of hepatocellular carcinoma (HCC) has not been well studied. We determined VOCs profile in exhaled breath of 97 HCC patients and 111 controls using gas chromatography–mass spectrometry and Support Vector Machine algorithm. The combination of acetone, 1,4-pentadiene, methylene chloride, benzene, phenol and allyl methyl sulfide provided the highest accuracy of 79.6%, with 76.5% sensitivity and 82.7% specificity in the training set; and 55.4% accuracy, 44.0% sensitivity, and 75.0% specificity in the test set. This combination was correlated with the HCC stages demonstrating by the increased distance from the classification boundary when the stage advanced. For early HCC detection, d-limonene provided a 62.8% sensitivity, 51.8% specificity and 54.9% accuracy. The levels of acetone, butane and dimethyl sulfide were significantly altered after treatment. Patients with complete response had a greater decreased acetone level than those with remaining tumor post-treatment (73.38 ± 56.76 vs. 17.11 ± 58.86 (× 106 AU, p = 0.006). Using a cutoff of 35.9 × 106 AU, the reduction in acetone level predicted treatment response with 77.3% sensitivity, 83.3% specificity, 79.4%, accuracy, and AUC of 0.784. This study demonstrates the feasibility of exhaled VOCs as a non-invasive tool for diagnosis, monitoring of HCC progression and treatment response.

VOCs from Exhaled Breath for the Diagnosis of Hepatocellular Carcinoma

“…Among the five VOCs that distinguished HCC patients from those without HCC, the levels of the two VOCs, acetone dimer and isopropyl alcohol, were significantly greater in HCC patients than in non-HCC patients. These observations were in line with previous studies [18,19] A study including 112 HCC patients, 30 cirrhotic patients, and 54 participants without chronic liver diseases reported that the level of acetone in the exhaled breath was found to be significantly higher in HCC patients and cirrhotic patients than participants without chronic liver diseases. Moreover, a combination of 18 exhaled VOCs, which included acetone and isopropyl alcohol, provided 73% sensitivity and 71% specificity for discriminating HCC patients from non-HCC individuals without cirrhosis [18].…”

“…The performance of exhaled VOCs for HCC diagnosis observed in this study (70.0% sensitivity, 88.6% specificity) was consistent with those shown in previous studies, with reported sensitivities ranging from 73.0% to 86.7% and specificities ranging from 71.0% to 91.7% [15,[17][18][19]. Likewise, the performance of VOCs in differentiating early HCC from advanced HCC (78.6% sensitivity, 88.9% specificity) was comparable to the previously reported sensitivity of 76.5% and specificity of 82.7% [19]. These findings support the utility of VOC analysis for HCC diagnosis.…”

“…When acetone was combined with another five exhaled VOCs, the combination of VOCs showed 76.5% sensitivity and 82.7% specificity for HCC diagnosis. Similar to the previous study, this study reported a significant reduction in acetone levels after HCC therapy, with a greater reduction in patients who responded to therapy when compared to those who did not respond to the treatment [19]. Taken together, these findings highlight the potential role of acetone as a biomarker for HCC diagnosis and monitoring treatment response.…”

“…Previous studies have shown that VOCs can be used as biomarkers for tumors with high mutational heterogeneity, such as HCC [15][16][17][18][19]. The feasibility of using exhaled VOC for detecting HCC was demonstrated in a study of scent detection by a dog reporting a sensitivity of 78% to differentiate HCC patients from healthy individuals [15].…”

“…Artificial intelligence (AI) has been increasingly developed and utilized in healthcare to assist medical personnel, such as computational pathology and chest x-ray screening [22]. Regarding VOC analysis, AI was applied to select the ideal combination of VOCs for diagnosing cancerous and non-cancerous diseases, e.g., tuberculosis, lung cancer, and HCC [19,[23][24][25]. Given the complexity and heterogeneity of tumor biology, the prediction model constructed using AI rather than conventional analyses is more capable of identifying the VOC pattern that differentiates cancer patients from individuals without cancer [26].…”