Volatile Organic Compounds for the Detection of Hepatocellular Carcinoma – a Systematic Review
Bibliographic record
Abstract
Abstract Background Hepatocellular carcinoma (HCC) is an increasingly common and one of the leading causes of cancer mortality worldwide. Only a small percentage of HCC patients are eligible to curative treatment. There is a need for a point of care, early diagnostic or screening tool. It is not clear whether exhaled volatile organic compounds (VOCs) could fulfil those needs. Hypothesis We postulate that exhaled VOCs can identify potential biomarkers for non-invasive detection of HCC. Aims This systematic review aims to critically review the current knowledge regarding the exhaled VOCs linked to HCC detection. Methods A systematic electronic search was conducted. Search strategy included all studied published until the 24th of March 2021 using a combination of relevant keywords. Results The search yielded 6 publications using the PRISMA pathway. Two of the studies described in vitro experiments, and four clinical studies were conducted on small groups of patients. Overall, 42 headspace gases were analysed in the in vitro studies. Combined, the clinical studies included 164 HCC patients and 260 controls. The studies reported potential role for a combination of VOCs in the diagnosis of HCC. However, only limonene, acetaldehyde and ethanol could be traced back to their biological pathways using KEGG pathway enrichment analysis. Conclusions Although there appears to be promise in VOCs research associated with HCC, there is no single volatile biomarker in exhaled breath attributed to HCC and data from extracted studies indicates a lack of standardization. Large population studies are required to verify the existence of VOCs linked to HCC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".