Large retrospective validation study of metabolomic biomarkers for resectable lung cancer detection and risk assessment.
Bibliographic record
Abstract
3071 Background: Cancer can be regarded as a metabolic disease and many studies published aimed at identifying robust metabolites for lung cancer diagnosis using plasma samples. Regardless of the lung cancer staging, most of these studies were performed on relatively small sample sizes. The purpose of this study is to validate whether a panel of metabolomic biomarkers would improve risk assessment for lung cancer detection in 800 plasma samples from patients that underwent lung cancer resection, and to understand the potential role and intersection between lung cancer and other lung diseases. Methods: A blinded case-control study was performed using plasma samples from 586 patients with biopsy-confirmed lung cancer compared to 214 controls from the same institutional biorepository to evaluate the performance of a 5+ metabolites biomarker panel for lung cancer detection. The control group consists of 90 healthy individuals and 124 with other non-neoplastic lung diseases including asthma, COPD, bronchiectasis and COVID. The lung cancer subgroups include early-stage (Stage I &II) adenocarcinoma and squamous cells carcinoma, advanced stages (Stage III & IV) NSCLC and neuroendocrine tumors. In this study, plasma metabolic profiles were performed using different liquid chromatography methods coupled with a targeted and quantitative mass spectrometry approach. Metabolite concentrations, clinical data, and smoking history were used to develop logistic regression models to identify lung cancer at different stages using significant biomarkers. The area under the receiver operator characteristic curves (AUC), sensitivities and specificities at selected cut off points were calculated for each cancer stage. Results: Univariate and multivariate statistical analyses confirmed the performance of our 5+ metabolites panel (β-HBA, PC aa C38:0, PC ae C40:6, citric acid, tryptophan, and etc.) as being significantly different between controls and lung cancer cases. Linear regression model using metabolites alone yielded an AUC of 0.89 for lung cancer at all stages. When adding smoking status, the models achieved an AUC of 0.91 with sensitivity of 91% and specificity over 78% using a risk prediction threshold of 0.657. Conclusions: The blood-based metabolites panel validated on our current retrospective study exhibited robust performance for resectable lung cancer detection and risk assessment. This metabolomic panel assay demonstrates potential diagnostic applications and clinical utility for patient selection that require further follow-up and confirmation using LDCT or other lung imaging modalities. The inclusion of other lung diseases in the control group is more representative of a real clinical setting suggesting that this blood-based assay could be offered to the medical community. Further studies may also confirm its utility in the context of a lung cancer screening program.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".