PD53-07 CLINICAL SIGNIFICANCE OF THE SIAα2,3GAL-GLYCOSYLATED PROSTATE-SPECIFIC ANTIGEN ASSAY FOR PROSTATE CANCER DETECTION
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: To reduce unnecessary prostate biopsies (Pbx), better discrimination is needed. To identify significant prostate cancer (sigPC) we determined the performance of Siaα2,3Gal-glycosylated prostate-specific antigen (S2,3PSA) and S2,3PSA normalized by prostate volume (S2,3PSAD). METHODS: We retrospectively measured S2,3PSA, total PSA (tPSA), and free PSA/tPSA (F/T PSA) values in 349 men who underwent a Pbx in three academic urology clinics in Japan and Canada (Pbx cohort). The assays were evaluated using the area under receiver operating characteristics curve (AUC) and decision curve analyses (DCA) to discriminate overall PC and sigPC. RESULTS: In the Pbx cohort, S2,3PSAD (AUC 0.795) provided significantly better clinical performance for discriminating overall PC compared with S2,3PSA (AUC 0.780, p <0.0001), PSAD (AUC 0.684, p <0.0001), tPSA (AUC 0.552, p <0.0001) and F/T PSA (AUC 0.689, p <0.0001). DCA analysis showed that using a risk threshold of 30%, adding S2,3PSA and S2,3PSAD to the base model (age, DRE status, tPSA, and F/T PSA) permitted avoidance of even more biopsies without missing PC (8.0% and 7.6% resp. vs. -0.3% (base model)). In addition, S2,3PSAD (AUC 0.827) provided significantly better clinical performance for discriminating sigPC compared with S2,3PSA (AUC 0.778, p <0.0001), PSAD (AUC 0.787, p <0.0001), tPSA (AUC 0.642,p <0.0001) and F/T PSA (AUC 0.686, p <0.0001). DCA analysis showed that using a risk threshold of 30%, adding S2,3PSA and S2,3PSAD to the base model permitted avoidance of even more biopsies without missing sigPC (14.0% and 13.2% resp. vs. 2.3% (base model)). CONCLUSIONS: The diagnostic performance of S2,3PSA is significantly better than the PSA, FT/ PSA & PSAD test in identifying patients with overall PC and sigPC. Addition of S2,3PSA test to conventional diagnostic model significantly improve avoidable biopsy effect in identifying patients with PC.Source of Funding: none
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".