Global Gleason grade groups in prostate cancer: concordance of biopsy and radical prostatectomy grades and predictors of upgrade and downgrade
Bibliographic record
Abstract
AIMS: To evaluate concordance, upgrades and downgrades from biopsy to prostatectomy, and associated clincopathological parameters, using the recently proposed Gleason grade groups/International Society of Urologic Pathology (ISUP) grades. METHODS AND RESULTS: We evaluated 2529 patients who underwent biopsy and prostatectomy in our institution from 2005 to 2014. A global grade group (GR)/Gleason score (GS) was used. Factors associated with GR1/GS ≤6 upgrades and GR2/GS3 + 4 downgrades were analysed by multivariable logistic regression. The final GR/GS was identical with the biopsy GR/GS in 59.3% of cases, with the highest concordance for GR2 and GR5 and lowest for GR4. In GR1-5, identical grades were found in GR: (i) 47.6%, (ii) 73.6%, (iii) 52.8%, (iv) 21.4% and (v) 68.3%, respectively. Final GR was upgraded in 32.3% cases; in GR1-4: (i) 52.4%, (ii) 19.0%, (iii) 16.4% and (iv) 32.9%. Most frequent upgrades occurred from biopsy GR1 to prostatectomy GR2. A final GR downgrade was found in 8.3% cases. For individual GR2-5 the downgrades were found in GR: (i) 7.4%, (ii) 30.8%, (iii) 45.7% and (iv) 31.7%. Upgrades of biopsy GR1 were associated with: age ≥60 years, PSA density ≥0.2, ≥2 positive cores, ≥5% core tissue involvement and perineural invasion [area under receiver operating characteristic (ROC) curve 0.699]. Downgrades of biopsy GR2 correlated inversely with: age ≥60 years, PSA >10 ng/ml and ≥2 positive core (area under ROC curve 0.623). CONCLUSIONS: We found highest concordance for GR2 and GR5 and lowest for GR4. The baseline clinical variables associated with GR1 upgrades and GR2 downgrades may play a role in clinical decision-making.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".