Optimization of the 2014 Gleason grade grouping in a Canadian cohort of patients with localized prostate cancer
Bibliographic record
Abstract
OBJECTIVES: To evaluate the five-tier Gleason grade group (GG) scoring of prostate cancers adopted by the International Society of Urology Pathology (ISUP) in 2014, and to propose modifications to optimize its performance. PATIENTS AND METHODS: Data were obtained from PROCURE, a prospective cohort of patients with localized prostate cancer undergoing radical prostatectomy in Québec, 2006-2013. Surgical specimens were evaluated by genitourinary pathologists using 2014 ISUP criteria. Treatment failure was defined as biochemical recurrence and/or initiation of secondary, non-adjuvant therapy. Analyses were conducted using Kaplan-Meier methods, log-rank tests, Cox proportional hazards models and Harrell's concordance indices. RESULTS: A total of 1 917 patients were included, with a median follow-up of 69 months. The 5-year treatment failure rates were 9.6%, 23.5%, 43.1%, 52.6% and 84.3% in GG1-5, respectively (P < 0.001 when comparing GG2 with GG3). Treatment failure rates for patients in GG2 and GG3 with tertiary Gleason 5 pattern were higher than patients in the same group without a tertiary pattern (P < 0.001), but were similar to rates for patients in GGs 3 or 4 without a tertiary pattern (P > 0.3). Primary Gleason pattern (4/5) predicted treatment failure in GG5 (5-year failure rates 82.3% vs 97.1%, respectively; P = 0.001). The five-tier GG system had greater accuracy as a prognostic indicator compared with the four-tier system (Harrell's concordance index 0.716 vs 0.676). When upgrading patients in GG2/3 with tertiary Gleason 5 pattern to patients in GG3/4, and separating patients in GG5 by primary Gleason pattern, the Harrell's concordance index increased to 0.730. CONCLUSION: The five-tier GG system increased accuracy for predicting treatment failure compared with the previous grading systems, but can be further improved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".