Utility of a Fifth-Generation Ultrasensitive Prostate-Specific Antigen Assay for Monitoring Prostate Cancer Patients after Radical Prostatectomy with 3 Years of Follow-Up
Bibliographic record
Abstract
BACKGROUND: We investigated an ultrasensitive prostate-specific antigen (uPSA) immunoassay (MesoScale; lower limit of detection (LLD) of 0.0035 pg/mL) to monitor patients with prostate cancer (PCa) following radical prostatectomy (RP) and to examine whether changes in PSA in the conventionally undetectable range (<1 pg/mL) can predict biochemical relapse (BCR). METHODS: We measured uPSA in serial serum samples (N = 100) collected from 20 RP cases with a third-generation ELISA (LLD of 1 pg/mL) and the fifth-generation MesoScale assay. We analyzed the PSA nadir changes to classify patients into BCR or non-BCR groups, observed the trends in PSA kinetics, and associated BCR status with clinicohistopathological features. RESULTS: The ELISA could quantify PSA in only 38% of the RP samples, detecting BCR in 7 of 20 patients with PCa. The MesoScale assay quantified PSA in all samples, showing 8 of 20 patients with BCR. However, there was no significant difference between the median time to BCR detection based on ELISA (1016 days) compared with MesoScale data (949 days). Gleason scores were higher in the BCR groups compared with non-BCR. There was no significant difference for other clinicohistopathological parameters. CONCLUSIONS: The uPSA MesoScale technology could track miniscule changes in serum PSA in the range of 0.003-1 pg/mL in all RP cases. However, PSA kinetics and nadir at concentrations <2 pg/mL fluctuated, and increases below this range could not reliably suggest signs of BCR. Instead, ultrasensitive fifth-generation PSA assays may hold clinical potential for measuring the low concentrations of PSA in women for various medical contexts.
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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) | 0.001 | 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".