Perineural invasion and TRUS findings are complementary in predicting prostate cancer biology.
Bibliographic record
Abstract
INTRODUCTION: Clinical variables with more accuracy to predict biologically insignificant prostate cancer are needed. We evaluated the combination of transrectal ultrasound-guided biopsy of the prostate (TRUSBx) pathologic and radiologic findings in their ability to predict the biologic potential of each prostate cancer. MATERIALS AND METHODS: A total of 1043 consecutive patients who underwent TRUSBx were reviewed. Using pathologic criteria, patients with prostate cancer (n = 529) and those treated with radical prostatectomy (RP) (n = 147) were grouped as: "insignificant" (Gleason score ≤ 6, prostate-specific antigen (PSA) density ≤ 0.15 ng/ml, tumor in ≤ 50% of any single core, and < 33% positive cores) and "significant" prostate cancer. TRUSBx imaging and pathology results were compared with the RP specimen to identify factors predictive of "insignificant" prostate cancer. RESULTS: TRUSBx pathology results demonstrated perineural invasion in 36.4% of "significant" versus 5.4% of "insignificant" prostate cancers (p < 0.01) and pathologic invasion of periprostatic tissue in 7% of significant versus 0% of insignificant prostate cancers (p < 0.01). TRUS findings concerning for neoplasia were associated with significant tumors (p < 0.01). Multivariable analysis demonstrated perineural invasion in the biopsy specimen (p = 0.03), PSA density (p = 0.02) and maximum tumor volume of any core (p = 0.02) were independently predictive of a significant prostate cancer. CONCLUSIONS: TRUS findings concerning for measurable tumor and perineural invasion in TRUSBx specimens appear to be complementary to Epstein's pathologic criteria and should be considered to aid in the determination whether a prostate cancer is organ-confined and more likely to be biologically insignificant.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".