Volatile organic compounds (VOCs) in biological samples originate both from exogenous and endogenous sources. Recent studies have highlighted their potential as cancer biomarkers, emphasizing the need for accurate detection methods in clinical settings. However, analysis of VOCs in whole blood (WB) samples remains challenging due to the complex matrix effects caused by the protein−VOC binding phenomenon and lack of standardized sample preparation protocols. Therefore, this study suggests a standardized method for advanced VOC analysis in WB samples specifically for veterinary applications. We compared 12 combinations of reagents composed of protein denaturing reagents and salts, particularly urea mixtures, to enhance VOC decoupling from proteins and improve matrix effect uniformity in gas chromatography−mass spectrometry (GC-MS) analysis. Among all combinations, urea with NaCl showed an optimal performance, demonstrating an advancement in the detection sensitivity of up to 151.3% and a significantly reduced matrix effect variation (−35.5% to 25%) compared with the water-only control. This novel approach eliminates complex procedures while maintaining accuracy, making it particularly suitable for veterinary uses. The method’s standardization and improved performance characteristics offer a practical solution for efficient VOC detection in veterinary diagnostics, potentially advancing tumor biomarker research.

Volatile organic compounds (VOCs) in biological samples originate both from exogenous and endogenous sources. Recent studies have highlighted their potential as cancer biomarkers, emphasizing the need for accurate detection methods in clinical settings. However, the analysis of VOCs in whole blood (WB) samples remains challenging due to complex matrix effects caused by protein-VOCs binding phenomenon and lack of standardized sample preparation protocols. Therefore, this study suggests a standardized method for advanced VOC analysis in WB samples specifically for veterinary applications. We compared 12 combinations of reagents composed of protein denaturing reagents and salts, particularly urea mixtures, to enhance VOC decoupling from proteins and improve matrix effect uniformity in gas chromatography-mass spectrometry (GC-MS) analysis. Among all combinations, urea with NaCl showed optimal performance, demonstrating advancement in the detection sensitivity up to 339.4% and significantly reduced matrix effect variation (-35.5% to 25%) compared to the water-only control. This novel approach eliminates complex procedures while maintaining accuracy, making it particularly suitable for veterinary uses. The method's standardization and improved performance characteristics offer a practical solution for efficient VOC detection in veterinary diagnostics, potentially advancing both environmental exposure monitoring and tumor biomarker research.

Canine cancer screening has an important limitation in that organ-specific screening strategies used in human medicine cannot be directly applied across routine veterinary clinical practice. Blood-based volatile organic compound (VOC) profiling may provide a practical approach for pan-cancer screening; however, its application in multicenter and multinational settings requires evaluation that accounts for cohort heterogeneity and analytical variability. In this study, we evaluated the canine cancer screening performance of an integrated classification framework using a headspace gas chromatography–mass spectrometry (HS-GC/MS)-based targeted blood VOC panel in a retrospective multicohort dataset comprising dogs recruited in Korea and Thailand. Whole-blood samples from 590 dogs recruited in Korea and Thailand were analyzed, including 167 dogs in the malignant tumor group and 423 dogs in the non-cancer control group. The predefined targeted VOC panel consisted of aromatic compounds and straight-chain alkanes and was analyzed using HS-GC/MS. The Korea and Thailand cohorts were incorporated into a single integrated classification framework rather than being separated into country-specific models. To evaluate performance variability associated with data partitioning, cohort-aware stratified training–validation splitting was repeated 30 times within the integrated retrospective cohort, with both national cohorts represented in the training and validation sets in each repeat. Class imbalance correction was applied only to the training data, and validation performance was assessed using both threshold-dependent and threshold-independent metrics. Across the 30 repeated validations, the integrated framework achieved an AUROC of 0.926 ± 0.019 and an AUPRC of 0.894 ± 0.023. Balanced accuracy, accuracy, sensitivity, specificity, F1-score, MCC, and Cohen’s κ were 0.858 ± 0.024, 0.895 ± 0.018, 0.772 ± 0.048, 0.943 ± 0.022, 0.805 ± 0.033, 0.736 ± 0.046, and 0.734 ± 0.045, respectively. In the sample-level prediction score analysis, the median predicted cancer probabilities for cancer and non-cancer samples were 0.954 and 0.041, respectively, indicating clear probability-based separation between the two groups. In the cohort-specific analysis, the discrimination signal was maintained in both the Korea and Thailand cohorts, although threshold-dependent performance differed between the two cohorts. These findings indicate that an HS-GC/MS-based targeted blood VOC panel provided a consistent cancer-associated classification signal under repeated internal validation within the specific integrated retrospective dataset evaluated in this study. The results demonstrate the feasibility of evaluating a common classification framework across the Korea and Thailand cohorts without constructing separate country-specific models, while not establishing independent external generalizability or platform-independent robustness. Because the Korea and Thailand cohorts were completely confounded with the Agilent and Thermo Fisher analytical platforms, respectively, the independent contributions of cohort characteristics and analytical platform cannot be determined from the present data. Further independent prospective validation in newly recruited populations and institutions, together with disease-control evaluation including clinically relevant controls, cohort-specific calibration, evaluation across analytical platforms, and assessment of clinical utility within real-world workflows, is required before the external generalizability and clinical applicability of the framework can be determined.