Comparison of liquid biopsy-based technologies for cancer screening
Bibliographic record
Abstract
Circulating plasma DNA has found important applications in diverse medical fields, including prenatal testing, transplantation, and especially cancer. Many companies have developed products for detecting minimal residual disease, selecting or monitoring therapy, assessing prognosis, and confirming diagnosis. One major application is in screening asymptomatic individuals for the presence of cancer. Screening may facilitate better clinical outcomes through earlier interventions. Collectively, these technologies are widely known as "liquid biopsies". After the extraction of free DNA from the circulation, it is analyzed by various molecular techniques to explore differences between DNA originating from normal cells and cancer cells. Circulating plasma DNA originating from tumors (ctDNA) is expected to harbor the same molecular changes as tumor tissue itself. Thus, ctDNA is considered a surrogate of cancer tissue, but without the need to perform invasive biopsies to obtain it. Many new diagnostic companies have taken advantage of this new biomarker and developed technologies for screening for one or multiple cancers. We previously estimated the amount of ctDNA in circulation, which is admixed with DNA originating from normal cells. We concluded that since only a small fraction of the whole plasma (3 liters) is used for testing (3 to 4 mL), it is possible that the retrieved ctDNA may not be enough for cancer diagnosis in all patients. This problem is more acute with small tumors. Here, we mention some companies in the "liquid biopsy" arena and analyze their clinical data to establish if their tests are close to entering the clinic. We conclude from this analysis that current data do not support the use of these technologies for population screening due to many false negative and false positive results.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".