Detection of lung cancer using expired breath analysis by ion mobility spectrometry
Bibliographic record
Abstract
Introduction: Volatile organic compounds (VOC9s) present in exhaled breath reflect the metabolic activity of the body and change in the presence of both respiratory disease and lung cancer. Human breath analysis is a non-invasive technique for the rapid identification of gas-phase analytes, such as VOC9s, and offers a new clinical diagnostic tool. Ion Mobility Spectrometry coupled with a Multi Capillary Column (MCC/IMS) allows exhaled breath to be rapidly analysed and characterised with great precision. Aim: To derive a database of exhaled breath profiles from participants with and without respiratory disease and to use this database to determine if breath borne VOC9s can be used to differentiate disease states. Methodology: Breath samples from 323 respiratory patients with and without lung cancer were measured using the MCC/IMS and analysed in real time (mean age 68 years, 195 males and 128 females). Demographic data, medical diagnosis and medical history were also collected. Results: The respiratory data were compared to control data from 182 healthy participants of the general public and NHS staff. All breath profiles collected were analysed using advanced chemometric techniques developed in collaboration with Radboud University, Nijmegen, Netherlands. This analysis determined which exhaled breath VOC9s have the potential to differentiate disease. Conclusion: The study has shown that lung cancer can be identified with 87% specificity and 70% sensitivity from healthy controls, reiterating the potential of breath analysis as a diagnosis tool.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".