Circulating tumour‐associated plasma DNA represents an independent and informative predictor of prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To investigate whether preoperative plasma levels of free DNA can discriminate between men with localized prostate cancer and benign prostatic hyperplasia (BPH). PATIENTS AND METHODS: In all, 161 referred patients suspicious for prostate cancer either by an elevated prostate-specific antigen (PSA) level and/or abnormal digital rectal examination (DRE) were included in this prospective study. Peripheral plasma was taken before prostate biopsy and genomic DNA was extracted from the plasma using the a commercial kit and a vacuum chamber. After controlling for age, PSA level, the percentage free/total (f/t) PSA and prostate volume, the median prostate cancer plasma DNA concentration served as diagnostic threshold in uni- and multivariate logistic regression models. Multivariate models were subjected to 200 bootstraps for internal validation and to reduce over-fit bias. RESULTS: Subgroups consisted of 142 men with clinically localized prostate cancer and 19 with BPH. The median plasma concentration of cell-free DNA was 267 ng/mL in men with BPH vs 709 ng/mL in men with prostate cancer. In univariate analyses, plasma DNA concentration was a statistically significant and informative predictor (P = 0.032 and predictive accuracy 0.643). In multivariate analyses, it remained statistically significant after controlling for age, tPSA, f/tPSA and prostate volume, increasing the predictive accuracy by 5.6%. CONCLUSIONS: Our data suggest that plasma DNA level is a highly accurate and informative predictor in uni- and multivariate models for the presence of prostate cancer on needle biopsy. The predictive accuracy was substantially increased by adding plasma DNA level. However, larger-scale studies are needed to further confirm its clinical impact on prostate cancer detection.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".