Clinical utility of a serum biomarker panel in distinguishing prostate cancer from benign prostate hyperplasia
Bibliographic record
Abstract
Prostate-specific antigen (PSA) screening for prostate cancer (PCa) is limited by the lack of specificity but is further complicated in the benign prostatic hyperplasia (BPH) population which also exhibit elevated PSA, representing a clear unmet need to distinguish BPH from PCa. Herein, we evaluated the utility of FLNA IP-MRM, age, and prostate volume to stratify men with BPH from those with PCa. Diagnostic performance of the biomarker panel was better than PSA alone in discriminating patients with negative biopsy from those with PCa, as well as those who have had multiple prior biopsies (AUC 0.75 and 0.87 compared to AUC of PSA alone 0.55 and 0.57 for patients who have had single compared to multiple negative biopsies, respectively). Of interest, in patients with PCa, the panel demonstrated improved performance than PSA alone in those with Gleason scores of 5-7 (AUC 0.76 vs. 0.56) and Gleason scores of 8-10 (AUC 0.74 vs. 0.47). With Gleason scores (8-10), the negative predictive value of the panel is 0.97, indicating potential to limit false negatives in aggressive cancers. Together, these data demonstrate the ability of the biomarker panel to perform better than PSA alone in men with BPH, thus preventing unnecessary biopsies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".