Percentage of high‐grade tumour volume does not meaningfully improve prediction of early biochemical recurrence after radical prostatectomy compared with <scp>G</scp> leason score
Bibliographic record
Abstract
OBJECTIVE: To examine whether percentage of tumour volume (%TV) and percentage of high-grade tumour volume (%HGTV) help to better identify men at higher risk of early biochemical recurrence (BCR) after radical prostatectomy (RP) for non-metastatic high-risk prostate cancer, as early BCR after RP might be associated with higher risk of metastases and cancer-specific mortality. PATIENTS AND METHODS: We examined the data of 595 men treated with RP for non-metastatic high-risk prostate cancer between 1992 and 2011 at two European tertiary care centres. Kaplan-Meier analyses were used to graphically depict 2-year BCR-free survival. Multivariable Cox regression models addressed early BCR. We tested whether addition of %TV and %HGTV to a multivariable Cox regression model helps to increase a model's predictive accuracy (PA) for prediction of early BCR. RESULTS: In all, 32 men (10%) with specimen-confined prostate cancer (pT2-pT3a, negative surgical margin, pN0) and 67 men (24%) with non-specimen-confined prostate cancer had early BCR. After stratification according to %HGTV (%HGTV threshold: ≤33.33 vs >33.33%), the 2-year BCR-free survival rates were respectively 93 vs 60% (log-rank P < 0.001). In multivariable Cox regression models %HGTV emerged as an independent predictor of early BCR (P < 0.001), whereas %TV did not (P > 0.05). However, adding %HGTV (regardless of its coding) to other covariates in multivariable Cox regression analysis did not increase the model's PA in a meaningful fashion compared with the use of the detailed Gleason grading system (6 vs 7a vs 7b vs 8 vs 9-10). CONCLUSIONS: In a large cohort of patients with high-risk prostate cancer, %HGTV and %TV did not improve prediction of early BCR after RP substantially, although %HGTV was an independent predictor of early BCR. Therefore, sophisticated TV/HGTV measurements do not seem to have additional benefit for early BCR prediction relative to the use of Gleason grading. However, these results need to be confirmed in larger, prospective studies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".