Characteristics of α2,3‐sialyl <i>N</i>‐glycosylated PSA as a biomarker for clinically significant prostate cancer in men with elevated PSA level
Bibliographic record
Abstract
BACKGROUND: The presence of glycosylated isoforms of prostate-specific antigen (PSA) in prostate cancer (PC) cells is a potential marker of their aggressiveness. We characterized the origin of α2,3-sialylated prostate-specific antigen (S23PSA) by tissue-based sialylation-related gene expression and studied the performance of S23PSA density (S23PSAD) alone and in combination with multiparametric magnetic resonance imaging (MRI) for the detection of clinically significant prostate cancer in men with elevated PSA. METHODS: Tissue-based quantification of S23PSA and sialyltransferase and sialidase gene expression was evaluated in 71 radical prostatectomy specimens. The diagnostic performance of S23PSAD was studied in 1099 men retrospectively enrolled in a multicenter systematic biopsy (SBx) cohort. We correlated the S23PSAD with Prostate Imaging Reporting and Data System (PI-RADS) scores in 98 men prospectively enrolled in a single-center MRI-targeted biopsy (MRI-TBx) cohort. The primary outcome was the PC-diagnostic performance of the S23PSAD, the secondary outcome was the avoidable biopsy rate of S23PSAD combined with DRE and total PSA (tPSA), and with or without PI-RADS. RESULTS: S23PSA was significantly higher in Gleason pattern 4 and 5 compared with benign prostate tissue. In the retrospective cohort, the performance of S23PSAD for detecting PC was superior to tPSA or PSA density (PSAD) (AUC: 0.7758 vs. 0.6360 and 0.7509, respectively). In the prospective cohort, S23PSAD was superior to tPSA, PSAD, and PI-RADS (AUC: 0.7725 vs. 0.5901, 0.7439 and 0.7305, respectively), and S23PSAD + PI-RADS + DRE + tPSA was superior to DRE + tPSA+PI-RADS with avoidance rate of MRI-TBx (13% vs. 1%) at 30% risk threshold. CONCLUSIONS: The diagnostic performance of S23PSAD was superior to conventional strategies but comparable to mpMRI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".