Comparison of Tumor Volume Parameters on Prostate Cancer Biopsies
Bibliographic record
Abstract
Context.— Prostate biopsy reports require an indication of prostate cancer volume. No consensus exists on the methodology of tumor volume reporting. Objective.— To compare the prognostic value of different biopsy prostate cancer volume parameters. Design.— Prostate biopsies of the European Randomized Study of Screening for Prostate Cancer were reviewed (n = 1031). Tumor volume was quantified in 6 ways: average estimated tumor percentage, measured total tumor length, average calculated tumor percentage, greatest tumor length, greatest tumor percentage, and average tumor percentage of all biopsies. Their prognostic value was determined by using either logistic regression for extraprostatic expansion (EPE) and surgical margin status after radical prostatectomy (RP), or Cox regression for biochemical recurrence-free survival (BCRFS) and disease-specific survival (DSS) after RP (n = 406) and radiation therapy (RT) (n = 508). Results.— All tumor volume parameters were significantly mutually correlated (R2 > 0.500, P < .001). None were predictive for EPE, surgical margin, or BCRFS after RP in multivariable analysis, including age, prostate-specific antigen, number of positive biopsies, and grade group. In contrast, all tumor volume parameters were significant predictors for BCRFS (all P < .05) and DSS (all P < .05) after RT, except greatest tumor length. In multivariable analysis including only all tumor volume parameters as covariates, calculated tumor length was the only predictor for EPE after RP (P = .02) and DSS after RT (P = .02). Conclusions.— All tumor volume parameters had comparable prognostic value and could be used in clinical practice. If tumor volume quantification is a threshold for treatment decision, calculated tumor length seems preferential, slightly outperforming the other parameters.
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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 | 0.010 |
| 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 | 0.000 |
| 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".