Concordance of systematic and fusion biopsy with surgical pathology.
Bibliographic record
Abstract
93 Background: Multiparametric MRI is increasingly used in prostate cancer detection. Previous studies have shown that detection rate of clinically significant cancer is higher in MRI targeted biopsy than systematic biopsy. However, the concordance between the Gleason score on fusion biopsy and radical prostatectomy is less well known. The objective of this study is to look for predictors of histopathologic concordance between biopsy (fusion and systematic) and radical prostatectomy. Methods: We used an institutional database of men who underwent mpMRI-ultrasound fusion targeted and systematic biopsy followed by radical prostatectomy. Gleason score on targeted, systematic and combination (targeted + systematic) biopsy were compared with Gleason score on radical prostatectomy, and concordance was recorded. The McNemar test was used to compare concordance and upgrade rates. Predictors of concordance and upgrade such as age, prostate volume, PSA, PSA density, Gleason score on biopsy, number of targets reported on mpMRI, and PI-RADS score were evaluated with Fisher’s exact test and logistic regression. Results: Surgical pathology was concordant with 47.4% of systematic biopsies, 52.0% of targeted biopsies and 58.4% of combination biopsies. There was no significant difference in concordance rates between systematic and targeted biopsy (P = 0.37). However, combination biopsy was superior to both systematic (RR 1.23, 95% CI 1.08-1.40, P = 0.03) and targeted biopsy (RR 1.12, 95% CI 1.02 – 1.24, P = 0.03) in predicting concordance with surgical pathology. Risk of upgrade to a higher Gleason score on surgical pathology was significantly lower with combination biopsy compared to systematic (RR 0.57, 95% CI 0.46-0.69, P < 0.001) or targeted biopsy alone (RR 0.72, 95% CI 0.61-0.84, P = 0.001). Upgrade rates were 43.9% for systematic biopsy, 34.7% for targeted, and 24.9% for combination. Lastly, we found no significant predictors of concordance or upgrade. Conclusions: Combination biopsy is associated with the highest concordance rate between biopsy and radical prostatectomy when compared with systematic or targeted biopsy alone. Performing targeted biopsy alone will underestimate tumour aggressiveness on surgical pathology.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".