How liquid biopsies can change clinical practice in oncology
Bibliographic record
Abstract
Cell-free DNA fragments are shed into the bloodstream by tumor cells. The analysis of circulating tumor DNA (ctDNA), commonly known as liquid biopsy, can be exploited for a variety of clinical applications. ctDNA is being used to genotype solid cancers non-invasively, to track tumor dynamics and to detect the emergence of drug resistance. In a few settings, liquid biopsies have already entered clinical practice. For example, ctDNA is used to guide treatment in a subset of lung cancers. In this review, we discuss how recent improvements in the sensitivity and accuracy of ctDNA analyses have led to unprecedented advances in this research field. We further consider what is required for the routine deployment of liquid biopsies in the clinical diagnostic space. We pinpoint technical hurdles that liquid biopsies have yet to overcome, including preanalytical and analytical challenges. We foresee how liquid biopsies will transform clinical practice: by complementing (or replacing) imaging to monitor treatment response and by detecting minimal residual disease after surgery with curative intent.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".