Non-invasive exploration of metabolic profile of lung cancer with Magnetic Resonance Spectroscopy and Mass Spectrometry
Bibliographic record
Abstract
BACKGROUND: Lung cancer is a major cause of global morbidity and mortality. Current low dose CT screening is invasive and its role remains contentious. There are no known biomarkers to monitor treatment response, detect disease recurrence and patient selection for adjuvant treatment after curative surgical resection. Hence there is an urgent need to explore non-conventional and non-invasive tools to develop novel biomarkers to improve the outcome of this lethal cancer. METHODS: This is an ongoing exploratory and translational study involving collection of bio fluids from 50 patients with early stage non-small cell lung cancer before and after surgical resection. The primary objective is to identify cancer specific metabolome in body fluids - sputum, exhaled breath condensate, blood and urine of the patients with early stage non-small cell lung cancer using Magnetic Resonance Spectroscopy and Mass Spectroscopy. CONCLUSION: The trajectory of change in metabolic profile of body fluids before and after surgical resection may have potential clinical applications in lung cancer screening, as biomarkers for disease recurrence and exploration of novel targets for therapeutic intervention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".