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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".