A Critical Analysis of the Tumor Volume Threshold for Clinically Insignificant Prostate Cancer Using a Data Set of a Randomized Screening Trial
Bibliographic record
Abstract
PURPOSE: The identification of clinically insignificant prostate cancer could help avoid overtreatment. Current criteria for insignificant prostate cancer use a tumor volume threshold of less than 0.5 ml for the index tumor. In this study we reassess this tumor volume threshold for clinically insignificant prostate cancer using an independent data set. MATERIALS AND METHODS: The rate of insignificant prostate cancer was calculated by modeling lifetime risk estimates of prostate cancer diagnosis in screened and nonscreened participants in a randomized prostate cancer screening trial. Using lifetime risk estimates 50.8% of screen detected prostate cancer was calculated to be clinically insignificant and the 49.2% largest tumor volume of 325 prostatectomy specimens was used to determine the threshold tumor volume for insignificant prostate cancer. Because stage and grade represent the strongest determinants of cancer aggressiveness, we also calculated the tumor volume threshold for insignificant cancer after the selection of patients with organ confined prostate cancer without Gleason pattern 4/5. The analyses were performed for total tumor volume and for index tumor volume. RESULTS: The minimum threshold tumor volume of the index tumor and total tumor was 0.55 and 0.70 ml, respectively. After accounting for tumor stage and grade we obtained a threshold volume for the index tumor and total tumor of 1.3 and 2.5 ml, respectively. CONCLUSIONS: We confirmed the original value of the index tumor volume threshold of 0.5 ml for insignificant prostate cancer, and we demonstrated that clinically insignificant prostate cancer may include index Gleason score 6, pT2 tumors with volumes up to at least 1.3 ml. These results suggest a reconsideration of current methods and nomograms used for pretreatment risk assessment.
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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 | 0.343 | 0.493 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".