Circulating tumor DNA (ctDNA) as a pan-cancer screening test: is it finally on the horizon?
Bibliographic record
Abstract
The detection of cancer at an early stage while it is curable by surgical resection is widely believed to be one of the most effective strategies for reducing cancer mortality. Hence, the intense interests in the development of a simple pan-cancer screening test. Lack of sensitivity and specificity when combined with the low prevalence of most types of cancer types in the general population limit the use of most of the existing protein biomarkers for this purpose. Like proteins, tumor DNA also can be released into the circulation. Such circulating tumor DNA (ctDNA) can be differentiated from normal cell DNA by the presence of specific genetic alteration such as mutations, copy number changes, altered methylation patterns or being present in different sized fragments. Emerging results with test such as CancerSEEK or GRAIL suggest that the use of ctDNA can detect cancer with specificities >99%. Sensitivity however, is cancer type and stage-dependent, varying from approximately 40% in stage I disease to approximately 80% in stage III disease. It is important to stress however, that most of the studies published to date have used patients with an established diagnosis of cancer while the control population were healthy individuals. Although the emerging results are promising, evidence of clinical utility will require demonstration of reduced mortality following evaluation in a prospective randomized screening trial.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".