Serum Protein Pattern Could Predict the Therapeutic Effect of First-Line Pemetrexed/Cisplatin Chemotherapy in Patients With Lung Adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: In patients with advanced non-squamous non-small cell lung cancer (NSCLC), a pemetrexed/cisplatin (PP) regimen is considered as one of the preferred first-line treatments. However, only about half of the treated patients respond, and there is no clinically useful marker that can predict the response to the regimen. METHODS: We established a potential pattern for the prediction of efficacy of first-line PP chemotherapy in patients with lung adenocarcinoma, by using artificial neural networks (ANNs) analysis of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) in this preliminary study. RESULTS: The samples were randomly divided into training set and test set. From the test set, through cross-validation, the established protein pattern for PP separated the responders from the non-responders with a sensitivity of 95.8% and a specificity of 90.0%. CONCLUSION: It could be helpful for oncologists to select patients who could benefit from PP chemotherapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".