The association between tumour density and prostate cancer recurrence following radical prostatectomy
Bibliographic record
Abstract
Purpose: Tumour density (TD) may be an independent prognosticfactor in men with prostate cancer. The purpose of this study wasto evaluate the association between prostate cancer TD and recurrencefollowing radical prostatectomy.Materials and Methods: Between 1995 and 2007, 645 patientsfrom The Ottawa Hospital or Memorial Sloan-Kettering CancerCenter who had cancer and prostate volumes measured from radicalprostatectomy specimens. Tumour density was defined as therelative tumour to prostate volume (tumour volume/prostate volume)and recurrence was defined as a prostate-specific antigen(PSA) >0.2 ng/mL and rising, or postoperative use of radiation orhormonal therapy. Associations between TD and recurrence areadjusted for preoperative PSA, prostatectomy Gleason sum, tumourstage and margin status.Results: Median follow-up was 40.8 months. Tumour density wasassociated with preoperative PSA, Gleason sum, tumour stage andsurgical margin status (all p < 0.0001). As a continuous variable,TD predicted recurrence-free survival (adjusted HR 1.34 per 10%increase in TD; p = 0.04). As a categorical variable, the groupof patients with a TD of >10% had a 2.7 times greater hazard ofrecurrence compared to patients with a TD <5% (95%CI 1.41,5.19; p = 0.003). Despite the independent association betweenTD and recurrence, the clinical value of TD remains in question asthe discriminative performance (area under the curve) of predictivemodels only improved from 0.865 to 0.876.Conclusions: Prostate cancer TD is associated with known prognosticfactors and is also independently predictive of recurrencefollowing radical prostatectomy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".