Clinical significance of the Lacdi<scp>NA</scp>c‐glycosylated prostate‐specific antigen assay for prostate cancer detection
Bibliographic record
Abstract
To reduce unnecessary prostate biopsies (Pbx), better discrimination is needed. To identify clinically significant prostate cancer (CSPC) we determined the performance of LacdiNAc-glycosylated prostate-specific antigen (LDN-PSA) and LDN-PSA normalized by prostate volume (LDN-PSAD). We retrospectively measured LDN-PSA, total PSA (tPSA), and free PSA/tPSA (F/T PSA) values in 718 men who underwent a Pbx in 3 academic urology clinics in Japan and Canada (Pbx cohort) and in 174 PC patients who subsequently underwent radical prostatectomy in Australia (preop-PSA cohort). The assays were evaluated using the area under the receiver operating characteristics curve (AUC) and decision curve analyses to discriminate CSPC. In the Pbx cohort, LDN-PSAD (AUC 0.860) provided significantly better clinical performance for discriminating CSPC compared with LDN-PSA (AUC 0.827, P = 0.0024), PSAD (AUC 0.809, P < 0.0001), tPSA (AUC 0.712, P < 0.0001), and F/T PSA (AUC 0.661, P < 0.0001). The decision curve analysis showed that using a risk threshold of 20% and adding LDN-PSA and LDN-PSAD to the base model (age, digital rectal examination status, tPSA, and F/T PSA) permitted avoidance of even more biopsies without missing CSPC (9.89% and 18.11%, respectively vs 2.23% [base model]). In the preop-PSA cohort, LDN-PSA values positively correlated with tumor volume and tPSA and were significantly higher in pT3, pathological Gleason score ≥ 7. Limitations include limited sample size, retrospective nature, and no family history information prior to biopsy. LacdiNAc-glycosylated PSA is significantly better than the conventional PSA test in identifying patients with CSPC. This study was approved by the ethics committee of each institution ("The Study about Carbohydrate Structure Change in Urological Disease"; approval no. 2014-195).
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".