Volatile organic compounds for the detection of hepatocellular carcinoma—A scoping review
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is an increasingly common and the second leading causes of cancer mortality worldwide with 5 years survival rate about 12%. Less than 20% of HCC patients are eligible to curative treatment owing to the late presentation. Clearly there is a need for a readily accessible, early screening tool. This scoping review critically appraises and synthesizes the current published knowledge about the use of exhaled VOCs as a potential non-invasive means for HCC detection aiming to advance this nascent field. A systematic electronic search was conducted. Search strategy included all studied published until the 24th of March 2023 using a combination of relevant keywords. The search yielded 9 publications using the PRISMA guidelines. Two of the studies described in vitro experiments, and seven 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 420 HCC patients and 630 controls. The studies reported potential role for a combination of VOCs in the diagnosis of HCC. However, there is lack of consensus. 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 multi-centre 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".