Cancer Screening Companies Are Rapidly Proliferating: Are They Ready for Business?
Bibliographic record
Abstract
Cancer screening has been a major research front for decades. The classical circulating biomarkers for cancer (such as PSA, CEA, CA125, AFP, etc.) are neither sensitive nor specific and are not recommended for population screening. Recently, circulating tumor DNA (ctDNA) emerged as a new pan-cancer tumor marker, with much promise for clinical applicability. ctDNA released by tumor cells can be used as a proxy of the tumor burden and molecular composition. It has been hypothesized that if ctDNA is extracted from plasma and analyzed for genetic changes, it may form the basis for a non-invasive cancer detection test. Lately, there has been a proliferation of "for-profit" companies that will soon offer cancer screening services. Here, we comment on Grail, Thrive, Guardant, Delfi, and Freenome. Previously, we identified some fundamental difficulties associated with this new technology. In addition, clinical trials are exclusively case-control studies. The sensitivities/specificities/predictive values of the new screening tests have not been well-defined or, the literature-reported values are rather poor. Despite these deficiencies some of the aforementioned companies are already testing patients. We predict that the premature use of ctDNA as a cancer screening tool may add another disappointment in the long history of this field.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.033 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".