Is prostate specific antigen (PSA) density necessary in selecting prostate cancer patients for active surveillance and what should be the cutoff in the Asian population?
Bibliographic record
Abstract
Background To investigate the role of Prostate Specific Antigen density (PSAD) in selecting prostate cancer patients for active surveillance (AS) and to determine a cutoff PSAD in identifying adverse pathological outcomes. Methods Data from 287 patients who underwent radical prostatectomy for prostate cancer were retrospectively reviewed. Six different AS protocols, the University of Toronto; Royal Marsden; John Hopkins; University of California San Francisco (UCSF); Memorial Sloan Kettering Cancer Center (MSKCC) and Prostate Cancer Research International: Active Surveillance (PRIAS), were applied to the cohort. Pre-operative demographics and pathological outcomes were analysed. Statistical analyses on the predictive factors of adverse pathological outcomes and significance of PSAD were performed. A cutoff PSAD with best balance between sensitivity and specificity in identifying adverse pathological outcome was determined. Results PSAD predicted adverse pathological outcomes better than Prostate Specific Antigen (PSA) level alone. The PSAD was significantly lower (0.12–0.13 ng/dl/ml) in protocols including PSAD (the John Hopkins and PRIAS) compared with the other four protocols not including PSAD as a selection criteria (0.21–0.25 ng/dl/dl, P = 0.00). PSAD predicted adverse pathological outcomes in all protocols not incorporating PSAD as an inclusion criteria ( P = 0.00–0.02). By the receiver operator characteristics curve analysis, it was found that a PSAD level of 0.19 ng/ml/ml had the best balance between sensitivity and specificity in predicting pathological adverse disease (Area under curve = 0.63, P = 0.004). Conclusion PSAD is necessary in selecting prostate cancer patients for active surveillance. It predicts adverse pathological outcomes in patients eligible for active surveillance better than PSA level alone. A PSAD cutoff at 0.19 ng/ml/ml has the best balance between sensitivity and specificity in predicting pathological adverse disease. We recommend using AS protocol incorporating PSAD as a selection criteria (in particular the PRIAS protocol with a cutoff PSAD at 0.2 ng/ml/ml) when recruiting prostate cancer patients for AS.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".