Serum and blood based biomarkers for lung cancer screening: a systematic review
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the second most common cancer and the leading cause of cancer death for both men and women. Although low-dose CT (LDCT) is recommended for lung cancer screening in high-risk populations and may decrease lung cancer mortality, there is a need to improve the accuracy of lung cancer screening to decrease over-diagnosis and morbidity. Blood and serum-based biomarkers, including EarlyCDT-lung and microRNA based biomarkers, are promising adjuncts to LDCT in lung cancer screening. We evaluated the diagnostic performance of EarlyCDT-lung, micro-RNA signature classifier (MSC), and miR-test, and their impact on lung cancer-related mortality and all-cause mortality. METHODS: References were identified using searches of PubMed, EMBASE, and Ovid Medline® from January 2000 to November 2015. Phase three or greater studies in the English language evaluating the diagnostic performance of EarlyCDT-lung, MSC, and miR-test were selected for inclusion. RESULTS: Three phase 3 studies were identified, one evaluating EarlyCDT-lung, one evaluating miR-Test, and one evaluating MSC respectively. No phase 4 or 5 studies were identified. All three biomarker assays show promise for the detection of lung cancer. MSC shows promise when used in conjunction with LDCT for lung cancer detection, achieving a positive likelihood ratio of 18.6 if both LDCT and MSC are positive, and a negative likelihood ratio of 0.03 if both LDCT and MSC are negative. However, there is a paucity of high-quality studies that can guide clinical implementation. CONCLUSIONS: There is currently no high quality evidence to support or guide the implementation of these biomarkers in clinical practice. Reports of further research at stages four and five for these, and other promising methods, is required.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".