VOCs from Exhaled Breath for the Diagnosis of Hepatocellular Carcinoma

Abstract

Background: Volatile organic compound (VOC) profiles as biomarkers for hepatocellular carcinoma (HCC) are understudied. We aimed to identify VOCs from the exhaled breath for HCC diagnosis and compare the performance of VOCs to alpha-fetoprotein (AFP). The performance of VOCs for predicting treatment response and the association between VOCs level and survival of HCC patients were also determined. Methods: VOCs from 124 HCC patients and 219 controls were identified using the XGBoost algorithm. ROC analysis was used to determine VOCs performance in differentiating HCC patients from controls and in discriminating treatment responders from non-responders. The association between VOCs and the survival of HCC patients was analyzed using Cox proportional hazard analysis. Results: The combination of 9 VOCs yielded 70.0% sensitivity, 88.6% specificity, and 75.0% accuracy for HCC diagnosis. When differentiating early HCC from cirrhotic patients, acetone dimer had a significantly higher AUC than AFP, i.e., 0.775 vs. 0.714, respectively, p = 0.001. Acetone dimer classified HCC patients into treatment responders and non-responders, with 95.7% sensitivity, 73.3% specificity, and 86.8% accuracy. Isopropyl alcohol was independently associated with the survival of HCC patients, with an adjusted hazard ratio of 7.23 (95%CI: 1.36–38.54), p = 0.020. Conclusions: Analysis of VOCs is a feasible noninvasive test for diagnosing and monitoring HCC treatment response.

Identification of breath volatile organic compounds to distinguish pancreatic adenocarcinoma, pancreatic cystic neoplasm, and patients without pancreatic lesions

“…The VOC extraction protocol was carried out using a previously validated method[ 21 ]. Extraction of VOCs from TD tubes was performed using a thermal desorption unit (Unity™-XR, Markes International Ltd, Llantrisant, United Kingdom).…”

“…VOC identification was achieved by calibrating the retention time of standard solutions with the chromatogram and comparing them with the in-house VOC data library[ 21 ]. The in-house VOC data library was created with XGBoost to identify important features of VOC profiles, and the algorithm chose the optimal combination of VOCs to yield optimal statistical parameters.…”

“…The in-house VOC data library was created with XGBoost to identify important features of VOC profiles, and the algorithm chose the optimal combination of VOCs to yield optimal statistical parameters. The XGBoost algorithm is an AI algorithm established in the previous VOC study[ 21 ]. XGBoost enabled the minimization of false predictions by continuously learning from the model that was previously constructed as a series and thus maximizing the prediction model's efficiency.…”

Alveolar gradients in breath analysis. A pilot study with comparison of room air and inhaled air by simultaneous measurements using ion mobility spectrometry

“…Measurements of inhaled air and room air were performed using IMS coupled with a MCC (MCC/IMS) (BioScout®-B & S Analytik GmbH, Dortmund, Germany). In the spectrometer, either a 550 MBq (Breath Discovery indicated as BD1) or a 95 MBq (indicated as BD2) 63 Ni β-radiation source was applied for the ionization of the drift gas. The difference in activity had no effect on the results because sufficient ionization of the analytes was achieved in all the cases.…”

“…To pentane [15,61], benzaldehyde [15,62], and acetone [63] as possible markers for carcinoma (lung, head and neck, hepatocellular carcinoma) and hematological diseases [64], the same applies as to benzene and 1-butanol. Furthermore, benzaldehyde and acetone have been reported as representative VOCs in a Covid-19 infection [65].…”