Data from Pentraxin-3 Is a Novel Biomarker of Lung Carcinoma
Bibliographic record
Abstract
<div>Abstract<p><b>Purpose:</b> Our objective was to validate the performance of three new candidate lung cancer biomarkers, pentraxin-3 (PTX3), human kallikrein 11 (KLK11), and progranulin.</p><p><b>Experimental Design:</b> We analyzed by commercial ELISA, and with a blinded protocol, 422 samples from 203 patients with lung carcinoma, 180 individuals with high risk for lung cancer (heavy smokers), and 43 individuals with cancers other than lung. All samples were obtained from the Early Detection Research Network (Reference set A).</p><p><b>Results:</b> We found that progranulin and KLK11 were not informative lung cancer biomarkers, with areas under the receiver operating characteristic curve (AUC; ROC), close to 0.50. However, PTX3 was an informative lung cancer biomarker, with considerable ability to separate lung cancer patients from high-risk controls. At 90% and 80% specificity, the sensitivities versus the high-risk control group were 37% and 48%, respectively. The discriminatory ability of PTX3 was about the same with all major subtypes and histotypes of lung cancer. The AUC of the ROC curves increased according to the disease stage, from 0.64 (stage I) to 0.72 (stage IV).</p><p><b>Conclusion:</b> PTX3, but not KLK11 or progranulin, is a new serum biomarker for lung carcinoma. Its diagnostic sensitivity and specificity is similar to other clinically used lung cancer biomarkers. More studies are needed to establish if PTX3 has clinical utility for lung cancer diagnosis and management. <i>Clin Cancer Res; 17(8); 2395–9. ©2011 AACR</i>.</p></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".