The role of PSA density to predict a pathological tumour upgrade between needle biopsy and radical prostatectomy for low risk clinical prostate cancer in the modified Gleason system era
Bibliographic record
Abstract
OBJECTIVES: We evaluate the role of prostate-specific antigen (PSA) density to predict Gleason score upgrade between prostate biopsy material and radical prostatectomy specimen examination in patients with low-risk prostate cancer. METHODS: Between January 2007 and November 2011, 133 low-risk patients underwent a radical prostatectomy. Using the modified Gleason criteria, tumour grade of the surgical specimens was examined and compared to the biopsy results. RESULTS: A tumour upgrade was noticed in 57 (42.9%) patients. Organ-confined disease was found in 110 (82.7%) patients, while extracapsular disease and seminal vesicles invasion was found in 19 (14.3%) and 4 (3.0%) patients, respectively. Positive surgical margins were reported in 23 (17.3%) patients. A statistical significant correlation between the preoperative PSA density value and postoperative upgrade was found (p = 0.001) and this observation had a predictive value (p = 0.002); this is in contrast to the other studied parameters which failed to reach significance, including PSA, percentage of cancer in biopsy and number of biopsy cores. Tumour upgrade was also highly associated with extracapsular cancer extension (p = 0.017) and the presence of positive surgical margins (p = 0.017). CONCLUSIONS: PSA density represents a strong predictor for Gleason score upgrade after radical prostatectomy in patients with clinical low-risk disease. Since tumour upgrade increases the potential for postoperative pathological adverse findings and prognosis, PSA density should be considered when treating and consulting patients with low-risk prostate cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".