Characterizing volatile organic compound profiles in oral cancer using multiple sample collection approaches by GC-IMS and TD-GC-MS
Bibliographic record
Abstract
Oral cancer (OC) is an aggressive malignancy with poor prognosis due to late-stage diagnosis and limited early detection tools. Volatile organic compounds (VOCs) have emerged as potential biomarkers for early OC detection, offering a non-invasive approach. However, the optimal sample collection and analytical workflow remains unclear. This study compares the diagnostic potential and clinical feasibility of exhaled breath, lesional air, and lesional brushings using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) and gas chromatography-ion mobility spectrometry (GC-IMS). Twenty-six participants (13 OC or high-grade lesion patients, 13 controls) were recruited. Multivariate analysis assessed group separation and identified key discriminatory features. TD-GC-MS detected more VOCs and demonstrated stronger separation between OC and controls across all sample types compared to GC-IMS. Lesional brushings provided the best separation between groups, followed by lesional air and exhaled breath. Key discriminatory compounds included various alkanes, alkenes, aromatic hydrocarbons, phenylmethanol, and a homologous series of saturated ketones, many of which have been reported as OC biomarkers. Lesional brushings and lesional air analyzed by TD-GC-MS emerged as the most promising approaches for OC detection. GC-IMS, despite limitations, holds potential as a valuable point-of-care OC screening tool. VOC-based diagnostics offer a promising non-invasive approach for early OC detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".