Primary Gleason pattern upgrading in contemporary patients with D'Amico low‐risk prostate cancer: implications for future biomarkers and imaging modalities
Bibliographic record
Abstract
Objective To retrospectively assess the rate of high‐grade primary Gleason upgrading ( HGPGU ) to primary Gleason pattern 4 or 5 in a contemporary cohort of patients with D'Amico low‐risk prostate cancer including those who fulfilled Prostate Cancer Research International Active Surveillance ( PRIAS ) criteria, and to develop a tool for HGPGU prediction. HGPGU is a contraindication in most active surveillance ( AS ) and focal therapy protocols. Patients and Methods In all, 10 616 patients with localised prostate cancer were treated at a high‐volume European tertiary care centre from 2010 to 2015 with radical prostatectomy. Analyses were restricted to 1 819 patients with D'Amico low‐risk prostate cancer (17.1%) with prostate‐specific antigen ( PSA ) levels of <10.0 ng/mL, cT 1c– cT 2a and Gleason score ≤6, and were repeated within 772 of the men (7.3%) who fulfilled the PRIAS criteria for AS ( PSA level of ≤10 ng/mL, T1c–T2, Gleason score ≤6, PSA density ( PSAD ) of <0.2 ng/mL 2 , ≤2 positive cores). Uni‐ and multivariable logistic regression models were fitted, testing predictors of HGPGU . The final logistic regression model was based on the most informative variables. Results There was HGPGU in 88 (4.8%) patients with D'Amico low‐risk prostate cancer and in 32 (4.1%) of the subgroup who were PRIAS eligible. Multivariable analysis predicting HGPGU for the patients with D'Amico low‐risk yielded three independent predictors: age, PSAD , and clinical tumour stage ( P = 0.008, P = 0.005 and P = 0.021, respectively). Within the same patients, the model using all vs the most informative variables resulted in area under the curves ( AUC s) of 69.2% and 68.3%, respectively. Multivariable analysis of those who were PRIAS eligible, yielded age and number of positive cores as independent predictors of HGPGU ( P = 0.002 and P = 0.049, respectively; AUC 64.9%). Conclusions The low accuracy (invariably <70%) for HGPGU prediction in both patients with D'Amico low‐risk prostate cancer and PRIAS eligibility indicates that these variables have poor predictive ability in contemporary patients. Despite HGPGU being a rare phenomenon, it may have life threatening implications and consequently alternatives such as biomarkers, genetic markers, or imaging modalities at re‐biopsy are needed.
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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 | 0.003 |
| 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 | 0.001 | 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".