Proton Magnetic Resonance Spectroscopy of Sputum for the Non-Invasive Diagnosis of Lung Cancer: Preliminary Findings
Bibliographic record
Abstract
Aims and Background: Sputum has been examined for the identification of potential biomarkers for the non-invasive diagnosis of lung cancer. However, no definitive biomarkers with reliable accuracy have been identified yet. The main objective of this work was to evaluate the utility of magnetic resonance spectroscopy (MRS) in the analysis of sputum for the non-invasive diagnosis of lung cancer. Methods: Induced sputum samples from lung cancer patients (n = 9) and control subjects (n = 6) were collected for proton (1H) MRS analysis. Samples from two cancer patients and one control subject were discarded as these samples were confirmed to contain only saliva by cytologic examination. Only the true sputum specimens containing alveolar macrophages were analyzed by 1H MRS. To facilitate MRS analysis, sputum samples were dispersed in 2M sodium chloride solution buffered with phosphate-buffered-saline (PBS). MR spectra were obtained using a one-pulse sequence with presaturation of the water resonance. Results: Glucose was found to be absent in sputum samples obtained from lung cancer patients. Spectra of sputum samples collected from control subjects showed presence of glucose signal except for one whose sputum cytology indicated the presence of atypia. The absence of glucose in sputum from cancer patients could be attributed to an increased rate of glycolysis in the lung cancer cells. The present observation, albeit on a small sample size, showed a better sensitivity (100%) and overall accuracy (92%) compared to sputum cytology (sensitivity = 50%; overall accuracy = 70%). Conclusions: Absence of glucose in sputum could be an indicator of lung cancer and the present methodology can be a valuable addition to the non-invasive diagnostics of lung cancer.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".